• Educational robotics for students and hobbyists
• Industrial automation requiring flexible robot programming
• Service robots in healthcare, hospitality, and retail sectors
• Research and development platforms for robotic innovations
• Assistive robotics for individuals with limited technical expertise

• Intuitive drag-and-drop graphical interface for robot programming
• Adaptive learning models that update as robots acquire new capabilities
• Comprehensive error analysis with user-friendly explanations
• Interactive training task recommendations to improve skills
• Integration of advanced motion planning, probabilistic modeling, and natural language processing
• Eliminates the need for expert coding knowledge in robot programming

1. Beef two weeks supercooling: https://youtu.be/9Is8XpadURY

2. Tuna eight days supercooling: https://youtu.be/3XxtFUeOZck

3. Chicken supercooling with narration: http://youtu.be/qgRX0M2fvko

This technology can be incorporated into consumer appliances and has application in food storage and transportation, especially produce, as well as medical applications in the preservation or transport of living tissue. If you have interest in these applications or have other applications in mind, we would love to hear from you.

Background:

Energy efficiency in buildings, aerospace, extreme environments and many other sectors seek low cost, scalable, lightweight and mechanically strong superinsulation materials (thermal and acoustic). Superinsulation aerogels are one of the most efficient thermal insulation materials. Large-scale utilizations of aerogel have been prohibitive due to its complex supercritical drying process which avoids the capillary induced structural degradation during the drying. This invention uses sol-gel chemistry coupled with ambient pressure drying that significantly reduces cost, processing time and energy input for producing aerogel foams with pore size below 10nm as well as enabling foam-fiber composites including flexible thermal insulating papers, sheets and embedded functional textiles. 

Technology Overview:

University at Buffalo researchers have developed methods of making ceramic foam and foam-fiber composites using low cost sol-gel chemistry based on in-situ generation of a pore-forming gas and reaction of the precursor(s) which occur at ambient pressures or in sealed environments. The process can integrate fibers of various types including, for example, traditional ceramic insulation fibers or a wide variety of textiles and natural fibers, imparting the aerogel benefits to the added fibers. The novel invention eliminates the prior art of complex aerogel processing and VOC solvents involved in producing ceramic foams by conventional high-pressure super critical drying. Methods for making the material transparent have also been demonstrated.

https://buffalo.technologypublisher.com/files/sites/7376_inpart_image1.jpg

Source: Ludmila, https://stock.adobe.com/uk/314176514, stock.adobe.com

Figure 1. (a) Schematic illustration of the manufacturing process of C-FRAero with two main steps: (1) in situ cross-linking reaction of preaerogel precursor (HCl, CTAB micelles, urea, and sodium silicate) and nanofibers; (2) C-FRAero paper sheet via vacuum filtration. (b) Demonstration of C-FRAero sponge sheet with the scale bar 10 cm, and the inset bottom images showing the hydrophobic capability after in situ trichlorosilane surface coating. (c) X-ray computed tomography (CT) scan images of C-FRAero sheet 3d bulk. X−Y and Y−Z plane CT images show the nanofiber layer stacks of the sample. The Y−Z plane CT image shows the fiber-aerogel morphology of the layer.

Advantages:

• Low cost (>90%) method that enables scalable, roll-to-roll processes
• Superior thermal and acoustic insulation and fire-resistant performance - thermal conductivity as low as 0.0190 W m-1 K-1 and high mechanical integrity of the compressive strength of 100.56 MPa, enabling shape customization
• Exceptional soundproof properties (sound reduction by 28.3%, or 22.3 db at a thickness of 15 mm at frequency of 2000 Hz) vs. reference insulating foam
• Does not require the complex processing,  VOC solvents or conventional high-pressure super critical drying
• Significantly increases the R-value of insulating fibers, foams, papers and textile materials such as nylon, polyaramid or cellulose
• The highly insulating aerogel ceramic foam can also be made transparent.

Applications:

• Superinsulation for commercial and residential buildings, aerospace, and extreme environments
• Flexible textile fabrics with high insulation properties - extreme cold or extreme heat, protective outerwear for fire fighters and extreme temperature industrial applications
• High efficiency acoustic soundproofing materials
• High temperature, extreme temperature insulation wraps, blankets, sheets and papers

Intellectual Property Summary:

Pending Patent Application. Publication No. US2023/0061063A1

Stage of Development:

• Demonstration of process and sample materials in various forms including bulk, powder, paper, sheet, foam, fiber impregnated, textile/fabric composite, commercial building insulation composite

Licensing Status:

Available for licensing.

Publication Links:

Nano Letters 2020 20 (2), 1110-1116

Nano Letters 2020 20 (5), 3828-3835

Figure 1: 

http://buffalo.technologypublisher.com/files/sites/7376-graphic.png

(a) Schematic illustration of the manufacturing process of C-FRAero with two main steps: (1) in situ cross-linking reaction of preaerogel precursor (HCl, CTAB micelles, urea, and sodium silicate) and nanofibers; (2) C-FRAero paper sheet via vacuum filtration. (b) Demonstration of C-FRAero sponge sheet with the scale bar 10 cm, and the inset bottom images showing the hydrophobic capability after in situ trichlorosilane surface coating. (c) X-ray computed tomography (CT) scan images of C-FRAero sheet 3d bulk. X−Y and Y−Z plane CT images show the nanofiber layer stacks of the sample. The Y−Z plane CT image shows the fiber-aerogel morphology of the layer.

UCLA researchers have developed Gadfly, an AI-powered learning platform that strengthens critical thinking by engaging users in structured Socratic dialogue, evidence-grounded debate, and real-time reasoning assessment.

Background:

Strong critical-thinking, analytical-reasoning, and argumentation skills are essential across educational and professional settings, including higher education, legal practice, policy, healthcare education, and leadership training. However, many existing AI-enabled learning tools are designed primarily to provide answers, summarize information, or generate explanations for users.

Although these tools can improve access to information, they may not require users to construct, defend, evaluate, or revise their own reasoning. Learners may therefore receive useful answers without practicing the deeper skills needed to assess evidence, identify gaps in an argument, respond to counterarguments, or refine a position under scrutiny.

Traditional Socratic questioning, debate, and individualized instructor feedback can provide this type of active engagement, but these approaches are difficult to deliver consistently and at scale. There is a need for an interactive platform that actively challenges users’ reasoning, grounds the exchange in relevant source materials, and provides structured, real-time feedback on argument quality and development.

Innovation:

UCLA researchers have developed Gadfly, an AI platform designed to strengthen critical thinking through structured Socratic dialogue, evidence-grounded debate, and real-time reasoning assessment.

Rather than simply providing answers or summaries, Gadfly asks users to articulate a position, explain their reasoning, and respond to targeted challenges. The platform analyzes the user’s argument to identify areas that may warrant further examination, including unsupported assumptions, gaps in evidence, and opportunities for refinement.

Gadfly can generate targeted Socratic questions and counterarguments using designated source materials, such as course readings, legal cases, research literature, training documents, or other domain-specific content. This helps keep the dialogue grounded in the materials selected by the instructor, organization, or user.

During the interaction, Gadfly provides real-time assessment of debate quality and analyzes how the user’s reasoning develops in response to questions, counterevidence, and alternative perspectives. The platform can track changes in the user’s argument over the course of the dialogue and provide measurable indicators of reasoning and argument development.

By combining active argumentative engagement, source-grounded critique, and real-time assessment, Gadfly provides a scalable platform for practicing and evaluating higher-order reasoning skills across academic, legal, professional, and other high-stakes training environments.

Potential Applications:

•    Higher education 
•    Legal education and professional development 
•    Law-firm training and advocacy preparation 
•    Corporate and professional training 
•    Scientific and health-professions education 
•    Policy and leadership training 

Advantages:

•    Promotes active rather than passive learning 
•    Provides individualized Socratic questioning at scale 
•    Grounds challenges in designated source materials 
•    Provides real-time assessment of debate quality 
•    Supports measurable analysis of reasoning development 
•    Adaptable across multiple academic and professional domains 

Development-To-Date:

Ongoing technical development and an initial UCLA classroom deployment, with early interest in additional university and professional pilots.

Related Papers:

• None currently listed.

Reference:

UCLA Case No. 2026-789

Lead Inventors:

Ladan Shams, David Kamper
 

Background 

Background:

The need for structural health monitoring (SHM) has recently become more critical due to public safety concerns, driven by increased natural disasters and aging infrastructure. As these factors elevate the risk of structural failure, continuous monitoring of civil infrastructure has become essential. However, current SHM technologies, such as accelerometers and vibration sensors fall short due to spatial and temporal precision limitations. Additionally, the adoption of advanced laser-based sensors is hindered by high costs and the logistics associated with their complex systems. There is a pressing demand for cost-effective, precise, and highly responsive civil SHM systems to enhance public safety.

Innovation:

To address these limitations, UCLA researchers developed a cost-effective, highly accurate, and universally deployable comprehensive system for SHM. This novel sensor system integrates diffractive optical layers with embedded damage identification algorithms. By doing so, it reduces dependence on costly conventional sensors, data acquisition hardware, and complex post-processing methods, ultimately simplifying costs and logistics. The diffractive optical layers govern light propagation and diffraction, optimized by the integrated algorithms, enabling statistical inference. Statistical inference improves data analysis and allows for more reliable conclusions about structural health. Additionally, lower costs and simplified logistics improve SHM coverage for civil infrastructure, enhancing safety and infrastructure lifespan, all while leveraging the latest advancements in sensor technology and data analytics. This innovative system has the potential to transform structural health monitoring by providing real-time updates, helping to prevent structural failures and enhance public safety.

Potential Applications:

•    Civil Infrastructure SHM
•    Bridges, roads, dams
•    Buildings, skyscrapers, historical monuments
•    Railroad tracks, airport runways
•    Wind turbines, power plants (cooling towers, reactor vessels, turbines)
•    Offshore/Marine SHM
•    Oil Platforms
•    Offshore rigs
•    Seismic Activity Monitoring
•    Disaster Management 

Advantages:

•    Spatial and temporal precision
•    Improved accuracy
•    Affordability
•    Suitable for large-scale projects
•    Scalable and universally deployable
•    Simplicity and reduced maintenance/downtime

Development-To-Date:

Successful experimental demonstrations and conducted cost-analysis

Related Papers:

[1] Structural Vibration Monitoring with Diffractive Optical Processors
https://arxiv.org/abs/2506.03317

[2] Lin X, Rivenson Y, Yardimci NT, Veli M, Luo Y, Jarrahi M, Ozcan A (2018) All-optical machine learning using diffractive deep neural networks. Science, 361(6406) https://doi.org/10.1126/science.aat8084  

[3] Kulce O, Mengu D, Rivenson Y, Ozcan A (2021) All-optical information-processing capacity of diffractive surfaces. Light: Science and Applications, 10(1) https://doi.org/10.1038/s41377-020-00439-9  

Reference:

UCLA Case No. 2025-201

Lead Inventor:

Aydogan Ozcan
 

Background:

Hypersomnia and excessive daytime sleepiness (EDS) are both symptoms of a broad class of sleeping disorders including obstructive sleep apnea (OSA), circadian rhythm disturbances, and narcolepsy. These conditions can impair personal health and quality of life and pose public safety risks, particularly in situations where sustained vigilance is necessary. Several pharmacological and behavioral interventions have been developed, but real-time methods of detecting and mitigating changes in alertness remain limited. Existing technologies are either therapeutic or diagnostic and tend to be bulky and non-wearable. Other systems monitor a single physiological marker (e.g., heart rate) and as such lack the multimodal sensing and signal analysis required to detect the nuanced changes associated with drowsiness. Additionally, these technologies do not provide active countermeasures in real-time to mitigate undesired changes in alertness. There remains an unmet need for a wearable device that monitors and predicts changes in alertness and provides cues that can prevent unwanted sleepiness in a wide array of fields.

Innovation:

UCLA researchers from the Department of Medicine-pulmonary disease have a developed a novel lightweight and wearable Alertness-Detecting Headset (ADH) that measures physiological signals from the head and face to detect and predict the onset of sleep. The headset integrates several sensors that monitor heart rate, changes in blood pressure, facial muscle tone and eye movement, and head orientation. These signals are processed in real-time and analyzed through artificial intelligence (AI) and machine learning (ML) algorithms that are trained to detect drowsiness and predict sleep-onset. This novel device can additionally provide bone-conducting audio alerts and visual cues with a mobile device to rouse the user. This innovation can revolutionize cognitive health tracking by providing an integrated and scalable platform for neurophysiological function monitoring.

Potential Applications: 

•    Drowsiness detection 
•    Wearable EEG screening
•    Real-time tracking of sleep cycles
•    Facial muscle recovery monitoring
•    Non-invasive tracking
•    Transportation alertness (trucking, aviation, etc.)

Advantages: 

•    Multimodal physiological monitoring 
•    Predictive alertness detection
•    Wearable and lightweight
•    Scalable 

Development-To-Date:

Initial conception (10/01/2024)

Reference:

UCLA Case No. 2025-245

Lead Inventor:

Ravi S. Aysola, MD, Clinical Professor of Medicine
Chief, Sleep Medicine Section
Division of Pulmonary, Critical Care and Sleep Medicine
Director, UCLA Sleep Disorder Center

Background:

As demand for clean hydrogen production rises, microbial electrolysis systems (MES) continue to show promise for sustainable hydrogen production. Conventional MES require an external voltage to convert organic waste into hydrogen, reducing system efficiency and off-grid applications. To address these limitations, bias-free MES designs have been explored, which utilize no external voltage by relying on energy released during microbial oxidation. Current bias-free MES are limited by scalability and low hydrogen yield because of locally low anode pH, hindering their practical applications. Moreover, advanced wastewater electrolysis systems rely on energy-intensive processes to drive the reactions, which diminishes their overall efficiency and benefit. To improve system performance and commercial viability of bias-free MES, a new design must be developed that significantly increases hydrogen production while enabling bias-free operation. 

Innovation:

Researchers at UCLA have developed a novel MES that utilizes a pH-decoupling design to achieve bias-free operation and enhanced system performance. Its architecture reduces energy requirements and enables bias-free hydrogen production. The microbial electrolysis cells can generate an electrical output of ~0.22 kWh m-3, supporting self-sustaining operation and dual-output functionality. Additionally, the system has a hydrogen production current of ~13-14 mA cm-2 and demonstrates long-term operational stability, maintaining a current density of 10 mA cm-2 for over 1,000 hours. These results highlight the system’s ability to sustain high-rate hydrogen production over extended periods, improving commercial viability. Overall, this bias-free MES combines high-rate hydrogen production, efficiency, and stability, positioning the technology as a compelling solution for next-generation sustainable hydrogen production. 

Potential Applications:

●    Self-powered hydrogen production
○    Decentralized, off-grid
●    Hydrogen fueling stations powered by organic waste
●    Integration in municipal/industrial wastewater treatment plants
○    Semiconductor processing plant wastewater treatment
●    Food/agricultural waste processing
●    Anaerobic digestion enhancement
○    Post-treatment for energy recovery
●    Biorefineries/chemical plants

Advantages:

●    Bias-free operation
●    Dual-output
○    Hydrogen and electrical power output
■    Multiple value streams from energy recovery
●    High performance
○    High-rate hydrogen production 
○    Stable operation (500+ hours at a high current density)
○    High Faradaic and Coulombic efficiency
●    Sustainable
●    pH-sensitive

Development-To-Date:

First successful demonstration of the invention completed August 2023.

Related Papers:

Huang, Y., et a. (2025). Biocatalyzed Lactate Oxidation Enables Efficient Bias-Free Hydrogen Production in a Three-Chamber Reactor; https://doi.org/10.1021/jacs.5c10688 

Huang, Y., et al. (2024). High power density redox-mediated Shewanella microbial flow fuel cells. Nature Communications. Advance online publication. https://doi.org/10.1038/s41467-024-52498-w 

Huang, Y., et al. (2021). Redox targeting of silver nanoparticles for enhanced extracellular electron transfer. Science, 373(6556), 653-656. https://doi.org/10.1126/science.abf3427

Reference:

UCLA Case No. 2025-298

Lead Inventors:

Yu Huang, Xiangfeng Duan
 

UCLA researchers in the Department of Electrical and Computer Engineering have developed a transistor-level method that dynamically tunes device characteristics to eliminate mismatch, achieving higher stability and precision without added area or power costs.

Background:

Transistors are the fundamental semiconductor building blocks for amplification, switching, and a wide range of digital processing and signal modulation applications. However, they are inherently subject to device-to-device mismatch and parametric variation due to process variability at the nanoscale. In many analog, mixed-signal, and RF circuits, precise device matching is essential for maintaining linearity, minimizing offset, and achieving optimal system performance. Fabrication defects further exacerbate these issues, leading to degraded performance, reduced yield, and the need for extra area or circuit-level compensation. Current approaches to mitigate mismatch include device sizing, common-centroid layout techniques, trimming, and digital calibration. While these approaches may reduce variability, they often introduce large demands in area, power, or complexity. There remains an unmet need for a method to intrinsically suppress mismatch effects, providing stable device characteristics without heavy reliance on external compensation or elaborate design techniques.

Innovation:

Professor Subu Iyer and his research team have developed a novel transistor-level method to substantially reduce circuit mismatch without incurring additional cost in area and power. Experimental results demonstrate a reduction in voltage-voltage mismatch by at least 100 mV, along with an expanded programmable threshold voltage dynamic range. The use of fully-depleted silicon-on-insulator (FD-SOI) transistors allows for increased electrostatic control of both the front gate and back gate of the transistor by coordinated modulation. By dynamically tuning the effective threshold voltage, mismatch can be suppressed at its source. Parameters such as leakage current and drive current can be adaptively adjusted such that unique variability cases inherent in semiconductor fabrication can be resolved. This capability represents a significant advancement, offering a pathway to greater stability, precision, and design flexibility in advanced circuit architectures.

Potential Applications:

• Analog and mixed-signal circuits
• RF front-ends and transceivers
• Precision amplifiers and data converters
• Low-power IoT devices
• High-speed digital logic

Advantages:

• Significant mismatch reduction
• Wider threshold voltage range
• Greater design flexibility
• Enhanced stability and precision
• No area or power costs

Status of Development:

First successful demonstration of the invention: December 2024.

Related Publications:

1. Xuefeng Gu & S. S. Iyer, “Fine-grained analog memory device based on charge-trapping in high-K gate dielectrics of transistors,” https://patents.google.com/patent/US10585643B2. (Granted 2020).
2. Frank Chang et al, “Memristive neural network computing engine using cmos-compatible charge-trap-transistor (ctt),” https://patents.google.com/patent/WO2019100036A1/. (Published 2019)
3. Steven Moran, et. al, “Neural network system with neurons including charge-trap transistors and neural integrators and methods therefore”. (Submitted 2021).

Reference:

UCLA Case No. 2025-271

Lead Inventor:

Subu Iyer, Distinguished Professor & Charles P. Reames Endowed Chair, Department of Electrical and Computer Engineering

Background:

Qubit arrays form the foundation of complex quantum computations required in modern quantum processors. Their use enables the key advantages of quantum computing, including high-speed information processing and exponential computational efficiency. A critical step in quantum computing is the process of qubit readout, which involves measuring the final state of each qubit after computation. However, readout is often limited by high error rates and signal interference when many qubits are measured simultaneously. To achieve reliable performance and improve computational output, it is essential to minimize qubit crosstalk and alleviate readout bandwidth bottlenecks. Existing solutions, such as cryogenic CMOS multiplexers, help reduce wiring complexity but can introduce additional electronic noise and consume substantial power at cryogenic temperatures. Improved microwave resonator readout schemes, including Purcell-filtered or frequency-multiplexed designs, enable faster measurements but are hardware-intensive and face scalability limits as qubit counts grow. There remains an unmet need for a scalable, low-power and low cross-talk approach to qubit readout suitable for large semiconductor-based 2D qubit arrays.

Innovation:

Professor Frank Chang and his research team have developed a novel qubit readout network enabling scalable, simultaneous measurement across a two-dimensional semiconductor qubit array. The system combines Code-Division Multiple Access (CDMA) and Multiple-Input Multiple-Output (MIMO) designs to enhance speed, fidelity, and resistance to interference. By multiplexing across both code and spatial domains, the technology supports large-scale readout with lower power demands and strong cryogenic performance. The readouts achieve >99% single-shot fidelity, ensuring accurate, fault tolerant operation. This solution offers a practical pathway toward integrating thousands of qubits on a single chip while maintaining efficient and reliable performance. This innovation significantly advances quantum processor scalability, overcoming key limits of current high-error low-efficiency readout systems.

Potential Applications:

●    Semiconductor-based quantum processors
●    Large-scale 2D qubit arrays
●    Fault-tolerant quantum computing systems
●    Quantum simulators for complex physical systems

Advantages:

●    Low power consumption at scale
●    High single-shot readout fidelity (>99%)
●    Scalable for large qubit arrays
●    Robust performance at cryogenic temperatures

Development Status:

First description of complete invention Nov. 16, 2024

Reference:

UCLA Case No. 2025-153

Lead Inventor:

Professor Mau-Chung Frank Chang, Department of Electrical and Computer Engineering
 

UCLA researchers have developed a subject-specific, deformable lung phantom that mimics both the mechanical (elastic) and radiological (attenuation) properties of a real human lung. The phantom is designed to replicate lung deformation during respiration, integrate imaging and computational fluid dynamics (CFD) data, and be used in radiotherapy quality assurance to improve treatment planning and patient outcomes. 

Background

For radiation therapy, accurately modeling how the lung and tumor move during breathing is critical to minimize radiation delivered to healthy tissue while ensuring the target receives the intended dose. Existing phantoms either approximate lung deformation poorly, lack accurate radiological properties (i.e. how they absorb/attenuate radiation), or are not tailored to specific patients. Also, imaging‐based deformation models (from CT or 4D‐CT) often lack validation and cannot always capture realistic mechanical behavior. There is a need for tools that more faithfully simulate patient-specific lung mechanics and radiological response for better QA of radiotherapy. 

Innovation

This invention combines:

• A material system comprising polymer nanocomposites embedding nanoparticles to achieve both lung-like elasticity and proper radiation attenuation.

• Use of imaging data (4D CT scans) from individual patients to reconstruct 3D lung geometry.

• Computational fluid dynamics (CFD) and flow–structure interaction (FSI) models to simulate spatio‐temporal lung deformation during breathing. 

• A mathematical data fusion approach (using Tikhonov regularization) that fuses imaging (inverse estimation) data with CFD predictions to optimize and validate deformation models for specific lung anatomy. 

• Fabrication of a physical lung phantom (including subject-specific elastic and radiological properties) that deforms in response to airflow or respiratory motion and behaves in imaging and radiation delivery consistent with modeled predictions. 

Advantages

• Custom, subject‐specific lung phantom that better matches patient lung geometry and elasticity. 

• Dual fidelity: both mechanical deformation and radiological attenuation properties are realistic.

• Enables more accurate QA for radiotherapy planning and delivery, especially under motion (breathing)

• Improves trust in deformation models by incorporating CFD and imaging fusion. 

• Allows testing and validation of radiotherapy plans, equipment, and delivery methods under realistic deforming conditions.

Potential Applications

• Radiotherapy planning and quality assurance for lung cancer patients, especially those with high respiratory motion.

• Validation of dose calculation algorithms that account for breathing motion or deformation.

• Development and testing of motion compensation strategies (e.g., gating, tracking).

• Use in training and calibration of imaging/radiotherapy devices.

• Research into lung mechanics, respiratory motion, and deformable image registration.

Patent

US 10,290,233 B2 — Physical Deformable Lung Phantom with Subject-Specific Elasticity Google Patents

UCLA researchers have invented a method and circuit architecture for fine-grained power gating within FPGA (field-programmable gate array) interconnects. By selectively disabling (power gating) unused multiplexers and routing segments at a fine granularity, the approach reduces leakage power in FPGA interconnects while preserving performance and flexibility.

Background

In modern FPGAs and programmable logic devices, much of the silicon area is used for interconnect routing (multiplexers, switches, wiring). Even when routing paths are unused, static leakage and parasitic capacitance cause wasted energy. Traditional power gating techniques often work at coarse granularity (entire blocks or macros), but lack flexibility to turn off unused interconnect segments without disrupting routing flexibility. There is a need for power-reduction techniques at the interconnect level that are fine-grained, dynamically configurable, and compatible with FPGA logic/routing architectures.

Innovation

• The invention embeds power gating control signals (PG_EN) into static multiplexers used in FPGA interconnect logic. These multiplexers include a standard selection logic plus an additional power gating enable input.

• When a multiplexer is not in use (i.e. its input is not selected), the power gating control disables (turns off) parts of the multiplexer’s output driver (e.g. via a PMOS transistor in cutoff mode) to cut leakage current.

• The design separates supply voltages (e.g. a high domain VDDH and a lower domain VDDL) to control logic and gating, enabling the gate of a PMOS transistor to be driven with minimal overhead while leaving routing logic dormant when unused.

• The architecture supports fine-grained, selective gating of small interconnect segments—multiplexers, inverters, and local routing switches—rather than coarse blocks.

• The invention is applicable not just inside FPGAs but to more general logic, configurable routing, or network-on-chip (NoC) systems with routing switches.

Advantages

• Reduces static (leakage) power consumption in the interconnect network, which is significant in scaled modern FPGAs.

• Operates at fine granularity: power gating can be applied at individual muxes or small routing elements rather than entire blocks.

• Minimal impact on performance: when active, gating is disabled and logic operates normally.

• Compatible with existing FPGA routing architectures and programmable logic design flows.

• Dynamic control: routing configuration logic can enable/disable gating based on use or mode, allowing runtime adaptation.

• Broad applicability to configurable logic, SoCs, and interconnect-heavy circuits beyond just FPGAs.

Potential Applications

• FPGA vendors seeking to reduce power consumption in their devices, especially in low-power or battery-powered contexts.

• Embedded systems and applications where static/leakage power is a major constraint (e.g. IoT, edge devices).

• SoC or NoC routing fabrics where routing switches are a large fraction of area and leakage.

• Reconfigurable computing platforms where resource usage is dynamic and idle interconnect should be gated.

• Power-aware design of configurable logic in data centers, AI accelerators, and reconfigurable hardware.

Patent / Application

US 9,923,555 B2 — Fine-Grained Power Gating in FPGA Interconnects
Fine-Grained Power Gating in FPGA Interconnects (US9923555B2) Google Patents

Researchers at UCLA have developed a lens-free, wide-field super-resolution imaging platform that uses a scanned illumination aperture and computational reconstruction to overcome the pixel size limit and achieve high-resolution imaging across large fields of view. The system captures multiple low-resolution holograms under shifted illumination and uses sub-pixel registration and algorithmic reconstruction to produce a high-resolution final image.

Background

Traditional optical microscopy systems often face a trade-off between field of view (FOV) and spatial resolution. When placing the sample very close to the sensor (on-chip holography), pixel size of the image sensor becomes a limiting factor, preventing resolution enhancement. At the same time, using magnification optics reduces the FOV and introduces complexity. To bring super-resolution imaging to compact, wide-area, lens-free systems, novel approaches are needed to circumvent the pixel-size constraint computationally, while maintaining large fields of view.

Innovation

• The system uses a scannable illumination aperture (e.g. a LED or laser source through a small aperture) that is raster scanned over multiple positions relative to the sample.

• Each scan position yields a lower-resolution hologram captured directly on the sensor, without lenses.

• Because the illumination shift corresponds to sub-pixel shifts at the sensor plane, the method recovers high spatial frequency information by combining these multiple holograms (i.e. a super-resolution reconstruction algorithm) to surpass the native sensor pixel limit.

• The algorithm includes self-calibration: the system does not require external knowledge of the scanning step. The shifts are inferred during reconstruction from raw holograms, simplifying hardware alignment and making the system robust.

• Because the illumination is partially coherent and from a relatively large aperture, the system suppresses speckle noise and interference artifacts compared to coherent-holography approaches.

• The reconstruction yields a numerical aperture ~0.5, enabling ~0.6 μm spatial resolution at visible wavelengths, while retaining a large imaging FOV (e.g. ~24 mm²) corresponding to the full detector area. 

Advantages

• Achieves high spatial resolution (sub-micron) even when limited by sensor pixel size.

• Maintains large field of view: FOV is equal to the active area of the detector, rather than reduced by magnification optics.

• Hardware simplicity: no refractive optics, lenses, or complex alignments required.

• Self-calibrating shift estimation reduces mechanical complexity and calibration burden.

• Reduced noise artifacts due to partially coherent illumination and large aperture usage.

• Scalable: larger sensor chips yield proportionally larger FOV at high resolution.

Potential Applications

• On-chip microscopy for cell biology: imaging live cells, pathogens, or microstructures over large fields.

• Portable diagnostic imaging systems, e.g. point-of-care microscopy.

• High-throughput screening platforms requiring wide-area imaging with sub-micron details.

• Environmental imaging (microplastics, particulates, organisms) in situ.

• Lab-on-chip devices combining imaging and microfluidics.

• Low-cost imaging devices for resource-limited settings (e.g. in field diagnostics).

Patent / Application

US 8,866,063 B2 — Lens-Free Wide-Field Super-Resolution Imaging Device
Lens-Free Wide-Field Super-Resolution Imaging Device (US8866063B2) Google Patents

Publications by the Inventors (Related Work)

• Bishara, W.; Rotella, C.; Ozcan, A. “Lens-free, pixel super-resolution, wide-field on-chip microscopy via self-assembled masks.” Optics Express 18(11): 11181-11191 (2010). DOI: 10.1364/OE.18.011181 — describes foundational techniques in mask scanning and computational super-resolution for lens-free imaging.

Summary

Researchers at UC San Diego have developed a compact, addressable flat-panel x-ray source based on pyroelectric / piezoelectric crystals with integrated field emitters. The design produces electron emission by temperature (or stress) cycling of crystals, which then strike a target to generate x-rays. The array architecture enables selectable activation of x-ray modules, potentially enabling portable, low-power, high-flexibility imaging systems without bulky high-voltage supplies.

Background

Conventional x-ray tubes require bulky high-voltage power supplies, vacuum systems, and cooling, limiting portability and flexibility. Miniaturized x-ray sources (microtubes or cold cathode arrays) still generally depend on large external electronics or complex designs. There is a need for a lightweight, modular, addressable x-ray source that is more portable, requires minimal external power, and can be used in settings like mobile diagnostics, field imaging, or bedside applications.

Innovation

• The invention uses pyroelectric (or piezoelectric) crystals coated with conductive films, into which micrometer-scale field emitter tips (sharp ridges or trenches) are milled or etched.

• By heating or cooling the crystals (or applying mechanical stress), spontaneous polarization changes generate perpendicular electric fields, which are enhanced at sharp emitter tips, causing field emission of electrons.

• These electrons accelerate across an evacuated gap and strike a bremsstrahlung target, producing x-rays.

• The design supports modular array architectures: multiple emitter modules (crystals) arranged in a planar array, each individually addressable (via temperature control) to produce x-ray emission in patterns.

• Optional spectral / spatial filters and collimators can be integrated to shape the x-ray output.

• The entire panel is designed to be compact, self-contained, and requiring minimal external power.

• Because the source doesn’t rely on large high-voltage supplies (the electric field arises internally via the pyroelectric effect), the device can be more lightweight and portable.

Advantages

• Compact, lightweight form factor, by eliminating large external HV supplies.

• Modular and addressable: individual pixels/modules can be selectively turned on or off.

• Reduced system complexity and potential for lower cost.

• Flexible emission patterns (line, area, or sweeping) for imaging flexibility.

• Potentially battery-powered or low-power operation in remote or field settings.

• Eliminates need for conventional x-ray tube infrastructure (e.g. vacuum pump, high-voltage generation).

• Scalable architecture: more modules can increase x-ray flux or coverage.

Potential Applications

• Portable medical imaging (e.g. in point-of-care, mobile clinic, bedside settings).

• Emergency / battlefield imaging for first responders or trauma care.

• Security and inspection systems (e.g. pack or luggage scanning, border checkpoint imaging) using lightweight panels.

• Dental / maxillofacial imaging in remote or resource-constrained environments.

• Integration into robotic platforms or drones for remote imaging / scanning tasks.

• Industrial non-destructive testing where localized x-ray emission can be deployed flexibly.

Patent / Record

US 8,755,493 B2 — Apparatus for producing X-rays for use in imaging
Apparatus for producing X-rays for use in imaging (US8755493B2) Google Patents

UCLA researchers have developed a high-speed, on-demand microfluidic droplet platform that allows rapid and precise generation, manipulation, and merging of microdroplets for advanced lab-on-a-chip applications. The technology enables controlled droplet initiation and synchronized merging at high throughput, making it a versatile platform for single-cell analysis, biochemical assays, drug discovery, and diagnostics.

Background

Microfluidic droplet systems are central to modern biomedical research and diagnostics, offering miniaturized reaction compartments for cells, nucleic acids, and chemical assays. However, most droplet systems rely on passive droplet formation methods with fixed geometries, producing droplets at preset frequencies with limited flexibility. This lack of control complicates workflows that require timed droplet creation, precise reagent addition, or multi-step reactions. A platform that enables on-demand droplet control and merging with high speed and precision would significantly expand the utility of droplet microfluidics.

Innovation

The patented system introduces:

• On-demand droplet generation using induced perturbations (e.g., acoustic, laser, or pressure triggers) within specially designed microfluidic geometries.

• High-speed operation, supporting rapid droplet creation synchronized with experimental workflows.

• Programmable droplet merging modules that bring together droplets from separate channels with precise timing, enabling multi-step assays in a single chip.

• Feedback and synchronization mechanisms (covered in the divisional patent) to improve merging reliability and throughput.

• Integration of multiple droplet functions—generation, merging, and reagent addition—on the same chip for end-to-end assay workflows.

Advantages

• Precise temporal control: droplets can be generated or merged at specific times rather than at fixed frequencies.

• High throughput: supports rapid workflows such as single-cell encapsulation and combinatorial assays.

• Scalable and modular: parallelization increases throughput; merging units enable complex multi-step processes.

• Reduced cross-contamination: precise droplet control improves assay fidelity.

• Flexible reagent addition: enables workflows such as sequential reagent introduction or cell lysis plus downstream reaction steps.

• Robust and reliable: divisional claims add features that improve consistency in droplet merging and multi-stage droplet operations.

Potential Applications

• Single-cell genomics and proteomics: encapsulating individual cells with reagents for downstream molecular analysis.

• Digital PCR and nucleic acid assays: precise droplet compartmentalization and reagent merging for ultrasensitive detection.

• Drug discovery and screening: combinatorial mixing of drug candidates and targets in microdroplets.

• Synthetic biology: programmable multi-step reactions in discrete droplet units.

• Diagnostics: lab-on-chip assays for infectious disease or cancer biomarkers with minimal sample volumes.

Development to Date

• Prototype microfluidic devices fabricated and validated.

• Demonstrated high-speed on-demand droplet creation and reliable downstream merging.

• Integration potential established for single-cell and biochemical assay workflows.

Patent / Application

• US 10,071,359 B2 — High-speed on demand microfluidic droplet generation and manipulation
  Patent Link

• US 10,780,413 B2 — High-speed on demand microfluidic droplet generation and manipulation (Divisional)
  Patent Link

Researchers at UCLA have developed a continuous, whole-chip, three-dimensional dielectrophoretic (DEP) cell sorting device, integrating multi-layer microfluidics and electrode geometries to sort cells or particles throughout the thickness of a microfluidic chip, not merely at a single plane.

Background

Microfluidic cell sorting is vital in many biomedical applications (e.g. diagnostics, single cell analysis, cell therapy). Conventional DEP sorters typically operate in 2D or at single planar electrode interfaces, limiting throughput, spatial sorting depth, or the ability to manipulate cells across the full chip thickness. Particles outside the active plane may not be sorted effectively, and cells may drift vertically or bypass sorting electrodes. A 3D DEP sorter that can manipulate particles across the full channel depth in a continuous flow is highly desirable for robust, high-efficiency separation.

Innovation

• The invention implements stacked electrode layers and multi-passages within a microfluidic chip, creating non-uniform electric fields in three dimensions to exert DEP forces throughout the channel depth.

• Multiple fluidic passages are arranged in a “sideways-H” or multi-pass geometry so that cells or particles traverse several regions where DEP fields become effective.

• The design ensures that cells from one passage can migrate laterally (via DEP) into another passage over the vertical dimension, effectively sorting them across depth.

• The chip integrates insulating layers (e.g. PDMS or composite PDMS layers) to isolate electrode layers and shape field gradients.

• The method allows continuous, high-throughput sorting of cells or particles based on dielectric properties, not just size or hydrodynamic behavior.

Advantages

• True 3D sorting capability across the full chip thickness, not just at a single focal plane.

• Continuous operation, enabling processing of streams of cells/particles rather than batch or static steps.

• Greater throughput and yield compared to planar DEP sorters, since no particles are “lost” outside the active plane.

• Flexibility in geometry: multiple passages and electrode layers can be tailored to desired sorting thresholds.

• Integration potential for downstream microfluidic modules (e.g. analysis, culture, collection).

• Useful for enriched separation of subtle dielectric differences among cells/particles beyond size separation.

Potential Applications

• Cell sorting for research and clinical applications (e.g. isolating rare cell populations, circulating tumor cells).

• Particle or bead sorting in microfluidic assays.

• Sample preparation in single-cell genomics, proteomics, or cytometry.

• Lab-on-chip systems where integrated sorting is needed prior to downstream analysis (e.g. PCR, imaging).

• Biomanufacturing or microfluidics in diagnostics, biotechnology, or pharmaceutical labs.

Patent / Record

US 9,770,721 B2 — Continuous Whole-Chip 3-Dimensional DEP Cell Sorter and Related Fabrication Method
Continuous whole-chip 3D DEP cell sorter (US9770721B2) Google Patents

UCLA researchers have developed a pulsed-laser triggered microfluidic switching mechanism capable of sub-100 µs switching (e.g. ~70 µs), enabling ultrafast cell/particle sorting in a microfluidic fluorescence-activated cell sorting (µFACS) platform using cavitation bubbles rather than mechanical valves.

Background

Flow cytometry and FACS are key tools in biology and medicine for sorting cells by fluorescence or other markers. Traditional droplet-based FACS systems are bulky, require open fluidics, high voltages, or mechanical parts, and may generate aerosols or damage cells. Microfluidic FACS attempts to miniaturize and enclose the process, but existing microfluidic switching (e.g. pneumatic valves, electrokinetic flows) are slow (hundreds of µs to ms), limiting throughput. There is a need for a fast, safe, compact, high-throughput switching mechanism compatible with microfluidic chips.

Innovation

• The switch uses a focused pulsed laser to induce a cavitation bubble adjacent to a microfluidic bifurcation. The rapid expansion of the bubble displaces fluid, deforming channel walls or injecting a micro-jet to redirect the trajectory of nearby particles/cells from one flow path to another. 

• By timing the bubble formation precisely after detecting a target particle or cell (e.g. via fluorescence), the system can sort individual particles into a “collection” channel rather than waste. 

• The design is compatible with single-layer PDMS microfluidic chips (no complex multi-layer valves required). 

• The switch is designed such that cells are shielded from direct impact of the bubble expansion/collapse, minimizing shear or stress damage. 

• The mechanism supports very high sorting speeds (greater than 10,000 cells/sec in some embodiments), significantly improving over prior microfluidic switches. 

Advantages

• Extremely fast switching (on the order of 70 µs or potentially faster). 

• Compact and chip-integratable (compatible with PDMS microfluidics). 

• No mechanical moving parts, eliminating many failure modes and enabling high reliability.

• Enclosed fluidics reduce aerosolization risk, improving biosafety. 

• High viability of sorted cells due to minimal mechanical stress.

• Scalable to multiple switches or parallel channels.

Potential Applications

• High-throughput microfluidic FACS modules in lab-on-chip devices.

• Cell sorting for diagnostics, single-cell genomics, immunology, or circulating tumor cell (CTC) isolation.

• On-chip sample preparation and sorting in integrated workflows (e.g. sorting → lysis → PCR).

• Portable or point-of-care cytometry systems where compactness and safety are key.

• Research platforms for rare cell detection, cell phenotyping, and personalized medicine.

Patent / Record

US 9,364,831 B2 — Pulsed laser triggered high-speed microfluidic switch and applications in fluorescent activated cell sorting
Pulsed-Laser Triggered Microfluidic Switch (US9364831B2)

Publication

T. -H. Wu, Y. Chen, S. -Y. Park and E. P. -Y. Chiou, "Pulsed laser triggered high speed microfluidic fluorescence activated cell sorter," CLEO: 2011 - Laser Science to Photonic Applications, Baltimore, MD, USA, 2011, pp. 1-2. https://ieeexplore.ieee.org/document/5950207

The combined technology platform encompasses optical cytometry via interferometric membrane probing and cell mechanical / membrane property characterization, using reflective microparticles and interferometric imaging to measure deformation and viscoelastic response of live cells. The approach links physical membrane dynamics to cellular state, enabling label-free diagnostics, mechanical phenotyping, or response monitoring. One of the patents (US 10,802,012 B2) applies interferometry with microparticles to observe membrane motion in response to magnetic actuation to infer cell viscoelastic properties and physiological state.

Background

Understanding mechanical and morphological properties of cells is increasingly important in diagnostics, drug screening, and cell biology. Traditional methods (e.g. micropipette aspiration, atomic force microscopy, traction force microscopy) are often low throughput, invasive, or require specialized instrumentation. Meanwhile, many optical cytometry techniques focus on fluorescence markers or bulk properties, lacking direct access to mechanical responses. There is a need for non-invasive, label-free, high-throughput methods that can probe mechanical and viscoelastic properties of cell membranes and correlate them to cell phenotype or response.

Innovation

• The patents introduce a method in which reflective microparticles (such as magnetic micromirrors) are adhered to or placed proximate to the cell membrane.

• The system uses interferometry (e.g. Michelson or similar configurations) to measure sub-nanometer displacements or motion of these microparticles in response to applied stimuli (e.g. magnetic force) or environmental perturbations.

• By analyzing the motion (amplitude, phase, dynamics) of the microparticles and relating them to applied force, the membrane mechanical properties, viscoelasticity, cell stiffness, or response dynamics can be inferred.

• The invention enables real-time or dynamic measurement of membrane movement and can correlate those mechanical properties to cellular states (e.g. healthy vs pathological, response to drugs).

• One embodiment involves applying a magnetic field to drive motion of the reflective microparticles, and observing the resulting membrane dynamics optically.

• The method is suited to measuring characteristics such as cell optical thickness, membrane displacement, mechanical deformation, or changes over time following stimuli.

Advantages

• Label-free mechanical phenotyping: does not rely on fluorescent dyes or genetic labels.

• High sensitivity: interferometric detection enables very fine displacement measurement (sub-nanometer).

• Potential throughput: integrated with optical cytometry, can analyze many cells in sequence.

• Non-destructive: minimal mechanical intrusion, preserving cell viability in many cases.

• Multiparametric: can measure both optical (thickness, refractive index) and mechanical (viscoelastic response) properties simultaneously.

• Correlative diagnostics: mechanical signatures can complement or even precede morphological or molecular markers.

Potential Applications

• Diagnostic assays where mechanical phenotype is indicative (e.g. cancer cell rigidity, metastatic potential).

• Drug screening: measuring how candidate compounds alter cell mechanics or membrane properties.

• Cell sorting or phenotyping platforms that incorporate mechanical contrast as a sorting criterion.

• Fundamental cell biology research: investigating mechanotransduction, membrane cytoskeleton integrity, or response to mechanical stimuli.

• Monitoring of cell state transitions (e.g. apoptosis, differentiation) via mechanical signature.

• Integration into optical cytometry devices (flow or static) for combined mechanical + optical characterization.

Patent / Applications

• US 10,802,012 B2 — Optical Cytometry to Determine Cell Characteristics via Membrane Interferometry & Microparticles Google Patents

• US 8,599,383 B2 — Optical Cytometry Google patents

• US 9,810,683 B2 — Use of live cell inteferometry with reflective floor of observation chamber to determine changes in mass of mammalian cells Google patents\

Several denoising algorithms, including MP-PCA and NORDIC, have been developed to separate true signal from random noise using statistical modeling. However, these tools were originally designed for diffusion MRI, a specialized sequence with hundreds of image contrasts. When applied to other MRI applications with fewer image contrasts or varied coil setups, these methods often fail to perform optimally, resulting in over-smoothed images, residual noise, or poor generalization. Due to these limitations in the state of the art, there remains a critical need for a robust, generalizable denoising solution that is compatible with multi-coil, multi-contrast MRI across a wide range of clinical and research applications.

Innovation:

UCLA researchers have developed a locally low-rank image denoising software that significantly improves image quality for multi-coil, multi-contrast MRI datasets. The software is structured in two main stages:

1.    Noise correction and normalization:
The preprocessing module directly handles raw imaging data to ensure that noise characteristics are consistent across all coils and contrasts used in GRAPPA reconstruction. This step effectively “normalizes” the noise to behave like Gaussian noise, the most predictable form, allowing more accurate and consistent denoising.
2.    Adaptive denoising algorithms:
The core denoising module separates signal from noise during image reconstruction. Two locally low-rank techniques are implemented. Both methods leverage the intrinsic signal structure within the data but differ in mathematical formulation, providing flexibility for different imaging conditions. The algorithms jointly process the multi-coil, multi-contrast data and generate denoised, coil-combined images as output.

Compared to existing open-source tools such as MP-PCA and NORDIC, this UCLA software uniquely integrates multi-coil noise correction, signal decorrelation, and g-factor map generation, enabling robust performance in datasets with limited contrasts. It also extends denoising capability beyond diffusion MRI to broader use cases such as cardiac, abdominal, interventional, and low-field MRI, where noise has inherently limited image quality. By improving the signal-to-noise ratio (SNR) without blurring or loss of detail, this technology enhances diagnostic reliability, supports advanced quantitative imaging, and can be seamlessly integrated into commercial MRI reconstruction pipelines or post-processing platforms. 

Potential Applications:

•    Cardiac MRI – Enhances visualization of heart structure and motion in accelerated scans
•    Abdominal and liver imaging – Improves image quality and SNR in motion-prone or deep-organ regions
•    Interventional MRI – Provides clearer, real-time visualization during minimally invasive procedures
•    Low-field MRI systems – Compensates for reduced SNR in portable or cost-efficient scanners
•    Neuro and musculoskeletal MRI – Improves structural detail and quantitative measurement reliability
•    Functional and emerging MRI applications – Benefits low-SNR modalities such as lung imaging and hyperpolarized MRI

Advantages:

•    Noise reduction without added scan time – improves SNR using intrinsic image structure instead of repeated signal averaging
•    Broad compatibility – applicable across diverse multi-coil, multi-contrast MRI sequences beyond diffusion imaging
•    Integrated noise calibration – accurately models noise behavior with built-in signal decorrelation and g-factor correction for GRAPPA-based parallel imaging accelerated reconstruction
•    Sharper, more reliable images – enhances diagnostic confidence by reducing noise without blurring fine details
•    Seamless integration – easily incorporated into commercial MRI reconstruction software or research workflows
•    Optimized for challenging conditions – maintains high performance in low-field, accelerated, or time-constrained imaging environments

State of Development:

The software has been validated on healthy volunteer MRI datasets. The implementation is available under an Academic Software License on GitHub: https://github.com/HoldenWuLab/LLR-image-denoising.

Related Publications:

Shih, Shu‐Fu, et al. "Improved liver fat and R2* quantification at 0.55 T using locally low‐rank denoising." Magnetic Resonance in Medicine 93.3 (2025): 1348-1364.

Reference:

UCLA Case No. 2025-056

Lead Inventors:

Shu-Fu Shih and Holden H. Wu from the Department of Radiological Sciences. 
 

UCLA researchers have developed a low-dose CT scanning system that uses pulsed X-ray emission at selected rotation angles combined with advanced reconstruction algorithms to maintain image quality while reducing radiation exposure by factors of ~4–8× compared to continuous CT protocols. 

Background

Computed tomography (CT) is widely used in medicine (e.g. perfusion CT, angiography), but continuous X-ray emission throughout the scan leads to substantial radiation dose to patients. Dynamic CT protocols (multiple time frames) multiply dose exposure. Conventional methods to lower dose often degrade image quality or temporal resolution. A method is needed to deliver substantially reduced radiation dose while preserving both spatial and temporal fidelity in CT imaging.

Innovation

The patented system modulates X-ray emission such that the CT source is intermittently pulsed (turned on/off rapidly) according to a predefined sequence of rotation angles of the CT gantry, rather than emitting continuously. During the “off” periods, no X-rays are emitted, reducing dose. To compensate, image reconstruction algorithms (including view sharing, iterative or constrained reconstructions) weave in data across frames and angles (e.g. using KWIC or projection-view sharing) to reconstruct high fidelity images. The system also permits selection of nonuniform projection angle sequences (e.g. angle-bisect / bit-reverse, golden ratio, pseudo-random), optimized for temporal/spatial sampling, and hardware that supports high-speed switching (on the order of milliseconds) of the X-ray source via pulsed control or shutters. 

Advantages

• Significant dose reduction (4–8× or more) while preserving image quality 

• Maintains both spatial and temporal resolution, enabling dynamic CT scans (e.g. perfusion) that were previously limited by dose constraints. 

• Flexible control over projection angle scheduling (angle-bisect, golden-ratio, pseudo-random) to optimize reconstruction and sampling. 

• Compatible with conventional CT hardware augmented with a pulse generator / shutter / beam gating mechanism. 

• Allows clinicians to trade off dose vs scan speed or temporal sampling automatically or adaptively.

Potential Applications

• CT perfusion imaging (brain, heart), where multiple time-resolved scans are needed

• CT angiography (dynamic vessel enhancement studies)

• Dynamic contrast-enhanced CT protocols in oncology (e.g. tumor perfusion)

• Any clinical CT scanning where reducing radiation dose is critical (pediatric, repeat imaging)

• Research or preclinical CT systems seeking dose-efficient dynamic imaging

• Low-dose CT in resource-limited settings where minimizing exposure is especially important

Patent / Record

US 10,772,579 B2 — Systems and Methods for Reducing Radiation Dose in CT
Systems and Methods for Reducing Radiation Dose in CT (US10772579B2) Google Patents

Spectral machine vision traditionally acquires dense 3D hypercubes (x, y, λ) of spatial and spectral information, which must be processed downstream. This imposes major computational burdens, tradeoffs in spatial/spectral resolution, frame rate, and power. The innovation embodied in spectral kernel machines (SKM) is a photodetector architecture that learns tasks in situ: instead of outputting raw spectra, the device’s photocurrent encodes the classification or identification result directly. By combining electrically tunable detection layers (e.g. bipolar black phosphorus–MoS₂ photodiodes for mid-IR, silicon photoconductors for visible) with internal kernel-machine–style processing, the SKM compresses spectral information and performs intelligent inference at the sensor level.

Advantages & Differentiators

• Ultra-low power: Projected up to 1,000× lower power consumption than conventional hyperspectral imaging + digital processing pipelines

• High speed: Estimated ~100× faster processing in hyperspectral task throughput versus existing solutions

• Compact, integrated inference: Shifts computational load into the photodetector itself, reducing data transfer, latency, and backend compute requirements. 

• Flexible spectral bands: Demonstrated in both visible and mid-IR (MIR) bands, enabling a wide range of sensing / spectral tasks. 

• Real-time “sniff-and-seek” operation: Learns from examples and classifies unknown samples with minimal data overhead. 

Applications / Use Cases

• Precision agriculture (e.g. plant health, nutrient / moisture sensing)

• Waste sorting and recycling (material discrimination)

• Food quality & safety inspection

• Pharmaceuticals and chemometrics

• Semiconductor wafer metrology and inspection

• Satellite, drone, or robotics spectral sensing in constrained power/latency environments

• Onboard hyperspectral machine vision in mobile or portable systems

Development Status

Proof-of-concept devices have been experimentally built (visible and MIR bands) and tested on real spectral classification and metrology tasks. Simulations and benchmark calculations suggest the performance advantages in power and speed are significant. Further development is needed for robustness, scaling, packaging, calibration, and system-level integration.

Lead/Inventor & Affiliations

Inventors: Dehui Zhang, Yuhang Li, Jamie Geng, Hyong Min Kim, et al. (with Aydogan Ozcan, Ali Javey)

Publications

Zhang, Dehui; Li, Yuhang; Geng, Jamie et al. (2025). Spectral kernel machines with electrically tunable photodetectors [Dataset]. Dryad. https://doi.org/10.5061/dryad.jh9w0vtpv

Researchers at UCLA have developed a smart EV charging and grid integration system that uses real-time control, state estimation, and bidirectional energy flows to manage EV charging and vehicle-to-grid (V2G) operations. The system optimizes charging schedules based on grid constraints, user preferences, and energy market signals while ensuring grid stability and battery health. 

Background

As EV adoption increases, uncontrolled charging at peak times can overload distribution infrastructure, cause transformer stress, and destabilize power networks. Utilities generally mitigate this by limiting charging or upgrading infrastructure—both costly approaches. A system that allows intelligent control of EV charging and enables EVs to act as distributed energy resources (e.g. discharging to grid when needed) can improve grid flexibility, reduce costs, and enhance grid reliability.

Innovation

• An expert system / control algorithm monitors and controls EV charging stations and connected vehicles, coordinating charging, discharging, and power flow in response to grid conditions, user charging demands, and preferences. 

• The system aggregates State of Charge (SOC) data, battery current/voltage sensors, and uses an Open Circuit Voltage (OCV) – SOC mapping to estimate battery SOC reliably under varying load profiles.

• The system supports bidirectional power flow (V2G), enabling vehicles to supply energy back to the grid during high-demand periods. 

• It integrates user preferences, cost signals, and power grid constraints, so that charging/discharging schedules are optimized (e.g. charging when rates are low, discharging when needed). 

• The invention includes hardware and software components in EVs and charging stations (e.g. controllers, communication, sensors) to support these smart behaviors. 

Advantages

• Mitigates grid overload by intelligently scheduling EV charging and discharging.

• Uses EV batteries as distributed energy storage (buffer) for grid demand response.

• Enhances utility flexibility without wholesale infrastructure upgrades.

• Better battery SOC estimation ensures safer, optimized charging/discharging.

• Improves cost efficiency for EV owners by leveraging low-cost energy periods.

• Increases integration of renewable energy (by time-shifting charging).

• Enhances grid reliability, voltage regulation, and stability by leveraging aggregated EV capacity.

Potential Applications

• Utility-scale deployment of smart EV charging in residential neighborhoods, workplace fleets, or public charging networks.

• V2G services to supply energy during peak demand or emergencies.

• Demand response programs where EVs participate in grid balancing.

• Microgrids or distributed energy systems that incorporate EVs as storage assets.

• EV fleet management (e.g. ride sharing, delivery) where charging patterns can be optimized to reduce grid impact.

• Renewable energy integration, where EVs buffer excess solar or wind generation.

Patent / Application

US 9,026,347 B2 — Smart Electric Vehicle (EV) Charging and Grid Integration Apparatus and Methods
Smart EV Charging & Grid Integration (US9026347B2) Google Patents

Background:

Minimally invasive surgery (MIS) has become the preferred standard for a wide range of medical procedures, driven by its benefits of reduced patient trauma, minimized scarring, and accelerated recovery. However, a critical challenge for the widespread adoption of MIS is maintaining clear visualization of the surgical area. Since MIS heavily relies on cameras, small traces of blood or bodily fluids can obscure the operative field, leading to prolonged procedure times and increased risk of surgical error. Current solutions for managing fluids include a separate suction device alongside small, rolled surgical sponges. These sponges quickly lose their effectiveness due to rapid saturation, requiring frequent removal and replacement. This process disrupts workflow and increases the risk of retaining a foreign object inside the patient. Requiring a dedicated suction device often involves swapping out essential surgical tools, adding even more risk and inefficiencies. Thus, there is a need for a multi-functional device that combines active suction and prolonged absorbance, allowing surgeons to maintain a clear operative field without the need for frequent tool exchanges or sponge replacement.

Innovation: 

UCLA researchers have developed a single-device fluid management system for minimally invasive surgery (MIS) that integrates an absorbent sponge tip, active suction, and an automated wringing mechanism. The biocompatible sponge tip serves as both an absorbent interface and a protective barrier over the suction lumen, eliminating suction-induced tissue trauma—a common complication with conventional suction instruments. Because the sponge tip is integrated into the device, it also removes the risk of retained surgical sponges.

The device incorporates an auto-drying mechanism consisting of a lever-actuated press plate, an accordion-style bellows housing that provides radial compression, and paired twist bars that apply rotational compression. This mechanism rapidly regenerates sponge absorbency without requiring sponge replacement, maintaining a clear operative field and improving procedural efficiency.

By unifying protection, suction, and regenerative absorbency in a single instrument, this innovation has the potential to significantly enhance MIS workflows, reduce complications associated with traditional fluid management approaches, and support broader adoption of minimally invasive procedures.

Potential Applications:

●    Minimally Invasive Surgery
●    Robotic Surgery
●    Laparoscopy
●    Thoracoscopy
●    General Surgery
●    Urologic Surgery
●    Gynecologic Surgery
     ○    Hysterectomy, ovarian cyst removal, endometriosis excision, etc.
●    Urology
     ○    Nephrectomy, prostatectomy, cystoscopy
●    Neurosurgery/Spine

Advantages:

●    Operational Efficiency
●    Clear Visualization
●    Enhanced patient safety
     ○    Compliance & Risk Reduction
●    Versatility

Development-To-Date:

First description of the complete invention.

Reference:

UCLA Case No. 2025-9AH

Lead Inventor:

Dr. Bryan Burt, UCLA Professor, Department of Surgery
 

Background:

Single-cell manipulation is essential for understanding cellular behavior, disease progression, and therapeutic response. Recent developments in single-cell research have facilitated the emergence of new applications including ex-vivo cell processing, which have revolutionized personal medicine, cellular research, and drug development. While microfluidic platforms enable precise cellular control at the microscale, current platforms are limited by low throughput and reliance on expensive instrumentation. Additionally, advances in genomics and proteomics require large-area manipulation of individual cells at scale while preserving cell viability and proliferation. These limitations in the state of the art highlight the need for a robust microfluidics platform that enables large-scale single-cell trapping and selective release for downstream single-cell analysis.

Innovation:

To address these challenges, researchers at UCLA have developed an acoustic microfluidic platform capable of precise and scalable single-cell manipulation. The device utilizes spherical air cavities to create acoustic potential wells that trap both individual synthetic microparticles and biological cells across a large area (1.5 x 1.5 cm²), accommodating a broad particle size range from 8 to 30µm. Additionally, the integration of a near-infrared laser facilitates high-throughput selective release of ~40 cells per minute while maintaining cell viability and proliferation. While currently demonstrated in a 1.5 x 1.5 cm² surface area, the manipulation area of this device may be scaled to tens or even hundreds of cm², enabling the trapping and release of millions of cells. This invention has a simple fabrication process without the need for photolithography, enabling low-cost development and disposability. This acoustic microfluidic system offers a robust, biocompatible, cost-effective, and scalable solution for single-cell manipulation, serving as a powerful new tool for high-resolution analysis in research and diagnostics.

Potential Applications:

●    Single-cell analysis
●    Genomics, proteomics
●    Cancer research
●    Immunology and cell therapy
     ○    Ex-Vivo cellular processing
     ○    CAR-T 
●    Drug discovery and development
     ○    Drug screening
     ○    Drug toxicology analysis 

Advantages:

●    High-throughput
●    Large-area single-cell manipulation
●    Selective Release
●    Individual cell targeting
●    Biocompatible
●    Scalability
●    Versatility
     ○    Broad size spectrum
●    Cost-effective
●    Simple fabrication
     ○    Disposable

Development-To-Date:  

This innovation was previously described by UCLA Case No. 2019-852: Mechanisms and Devices Enabling Arbitrarily Shaped, Deep-Subwavelength, Acoustic Patterning. Case No. 2025-047 now enables single cell resolution acoustic patterning not previously described by 2019-852. 

Related Papers:

•    Zhang, X., Smith, J., Zhou, A. C., Duong, J. T.-T., Qi, T., Chen, S., Lin, Y.-J., Gill, A., Lo, C.-H., Lin, N. Y. C., Wen, J., Lu, Y., & Chiou, P.-Y. (2025). Large-scale acoustic single cell trapping and selective releasing. Lab on a Chip, 25(6), 1537–1551. https://doi.org/10.1039/D4LC00736K

•    Tung, K.-W.; Chung, P.-S.; Wu, C.; Man, T.; Tiwari, S.; Wu, B.; Chou, Y. F.; Yang, F.-L.; Chiou, P. Y. Deep, subwavelength acoustic patterning of complex and non-periodic shapes on soft membranes supported by air cavities. Lab Chip 2020, 20, 2870–2879. DOI: 10.1039/C9LC00612E

•    Tung, K.-W., Chiou, P.Y.
Field-programmable acoustic array for patterning micro-objects, Applied Physics Letters, 116, 151901, 2020. DOI: 10.1063/5.0003147

•    Zhang, X., Sun, R., Lin, Y.-J., Gill, A., Chen, S., Qi, T., Choi, D., Wen, J., Lu, Y., Lin, N. Y.C., Chiou, P.Y. Rapid prototyping of functional acoustic devices using laser manufacturing, Lab on a Chip, 22, 4327–4334, 2022. DOI: 10.1039/d2lc00725h

•    US12,280,372B2: Arbitrarily shaped, deep sub-wavelength acoustic manipulation for microparticle and cell patterning

Reference:

UCLA Case No. 2025-047

Lead Inventor:

Pei-Yu “Eric” Chiou
 

Background:

Rare earth elements (REEs) are critical inputs for renewable energy technologies, electric vehicles, advanced electronics, and defense systems. However, their separation and extraction remain challenging due to the elements’ nearly identical physicochemical properties, frequent co-occurrence in ores, and typically low natural abundance. These factors make conventional extraction highly resource-intensive and costly. Current industrial separation techniques rely heavily on complex solvent-extraction processes that require large volumes of chemicals and substantial energy inputs. These methods often struggle to discriminate effectively among individual REEs, leading to inefficient recovery and the generation of significant chemical waste with associated economic and environmental burdens. Solid-phase material systems have been explored as alternatives, but many lack selectivity, operational reliability, and scalability needed for practical deployment.

Biologically inspired approaches, including the use of high-affinity proteins such as lanmodulin (LanM), demonstrate promise but have not yet been broadly validated across diverse metal targets or translated into scalable, industrially viable recovery platforms. With global demand for REEs continuing to rise, there is a clear need for an alternative separation technology that delivers high selectivity, reduced environmental impact, and cost-effective scalability.

Innovation: 

UCLA researchers have developed a peptide-based platform that enables highly selective separation and recovery of rare earth elements (REEs). These engineered peptides exhibit strong, preferential binding to light and medium REEs while demonstrating minimal affinity for competing non-REE species. In magnet-derived solutions representative of electronic waste streams, the peptides achieved 94.7 percent recovery of praseodymium and nearly complete recovery of neodymium. They also delivered greater than 90 percent REE recovery from low-grade feedstocks, underscoring their robustness and suitability for real-world processing environments. The peptides can be immobilized within column-based systems, providing reusability without sacrificing selectivity and enabling integration into scalable separation workflows. The inventors demonstrate that these novel peptides may serve as efficient bioligands for enhanced REE recovery development. This technology overcomes key limitations of conventional REE extraction methods by offering a selective, reusable, and industrially scalable approach to REE recovery.

Potential Applications:

●    Rare earth refining/purification
●    Electronic waste recycling
●    Low-grade ore processing
●    Sustainable REE production
●    Materials recovery

Advantages:

● High selectivity: Precisely targets light and medium REEs while minimizing non-REE binding
● High recovery efficiency: Demonstrates strong performance across electronic-waste solutions and low-grade feedstocks
● Reusability: Peptide immobilization supports multiple separation cycles without loss of selectivity
● Cost-effective: Reduces reliance on complex chemical processes and lowers operational expenses
● Scalable: Compatible with column-based and industrial separation workflows
● Modular: Can be integrated into existing recovery systems or adapted for customized separation schemes
● Sustainable: Enables cleaner REE extraction with reduced chemical waste and environmental impact

Development-To-Date:

Journal manuscript in preparation.

Related Papers:

Choi, Dasol, Wonhyeong Lee, Soyoung Choi, Loretta M. Roberson, José Avalos, and Aaron J. Moment. “Waste Sargassum Seaweed as a Sustainable Resource for Rare Earth Element Recovery.” ACS Sustainable Chemistry & Engineering, vol. 13, no. 47, 2025, pp. 20476–20485, American Chemical Society, https://doi.org/10.1021/acssuschemeng.5c08832

Reference:

UCLA Case No. 2026-031

Lead Inventor:

Aaron Moment
 

Researchers in UCLA’s Department of Mechanical and Aerospace Engineering have developed a novel transition metal compound that demonstrates what is thought to be the highest thermal conductivity reported among metallic materials. The material is synthesized as a single, defect-free crystal, enabling unprecedented efficiency in heat transport. This breakthrough offers exceptional heat dissipation performance and has broad applicability across heat-limited technologies, including advanced electronic systems, quantum computing hardware, and large-scale data center cooling infrastructure.

Background:

Efficient heat dissipation is essential across a wide range of electronics applications, including high-performance computing, batteries, RF and quantum devices, electric vehicles, AI data centers, power electronics, LED lighting, and medical equipment. While metallic materials can exhibit high thermal conductivity, their performance is inherently limited by intrinsic scattering mechanisms by electron-phonon and phonon-phonon interactions. Θ-phase tantalum nitride (Θ-TaN), a newly discovered metallic material, exhibits exceptionally high thermal conductivity due to its ultrastiff atomic bonding and low electron-phonon coupling. Prior attempts to stabilize Θ-TaN for practical use have been constrained by extreme pressure and temperature requirements, and polycrystalline samples often suffer from grain boundaries, point defects, and phase heterogeneity. There remains a strong unmet need for a stable high-thermal-conductivity Θ-TaN material that can be reliably integrated into advanced electronics applications.

Innovation:

Professor Yongjie Hu and his research team have developed a single-crystalline Θ-TaN with ultrahigh thermal conductivity of 1100 W/mK at room temperature, the highest reported for any metallic material and almost three times that of silver, copper, and silicon carbide. Using a flux-assisted synthesis approach, the team bypassed the extreme high-pressure and high-temperature conditions typically required for Θ-TaN stabilization. The resulting material has improved crystallinity, high phase purity, well-faceted grain morphology, and minimal defects. Comprehensive experimental characterization confirmed consistent diffraction patterns and uniform thermal conductivity throughout the entire crystal. 

The inventors demonstrate unprecedented phonon-dominated heat transport in a metallic system, overcoming limitations of scattering in the state of the art. Θ-TaN represents a new class of ultrahigh-thermal-conductivity metallic materials, with applications spanning numerous high-heat-flux industries. This breakthrough material has the potential to redefine thermal management across advanced electronics, aerospace systems, AI accelerators, and energy and power applications by delivering uniform, ultrahigh thermal conductivity through a defect-free crystalline structure that outperforms conventional metals.

Potential Applications:

●    Thermal management in high-performance computing systems
●    Cooling for RF amplifiers and quantum devices
●    Heat dissipation in AI and data center hardware
●    Battery and power electronics thermal control in electric vehicles
●    Temperature regulation in medical devices 
●    Thermal management in mobile and portable electronics 
●    Aerospace and defense thermal management systems

Advantages:

●    Highest thermal conductivity measured for any metallic material (1100 W/mK)
●    Uniform thermal performance across the crystal
●    Perfect single-crystalline structure with no grain boundaries
●    High phase purity and minimal defects
●    Well-faceted, stable crystal morphology 
●    Synthesized without need for extreme pressures or temperatures
●    Compatibility with current manufacturing processes
●    Mechanical robustness

State of Development:

First description of complete invention: 05/01/2023. High-quality single-crystal structure confirmed using Raman spectroscopy, S-XRD, HRTEM, and EELS.

Publication:

Suixuan Li et al., Metallic θ-phase tantalum nitride has a thermal conductivity triple that of copper.Science0,eaeb1142, DOI: 10.1126/science.aeb1142

Reference:

UCLA Case No. 2026-180

Lead Inventor:

Yongjie Hu, Faculty, Department of Mechanical and Aerospace Engineering
 

Background:

Advanced semiconductor systems rely on 3D integrated circuits (3DICs) to meet rising demands for higher performance, reduced form factor, and lower latency. Glass interposers are being adopted as substrates for advanced packaging systems due to their low cost and favorable electrical properties, including lower signal loss and scalability. But unlike the current standard of silicon, glass exhibits intrinsically low thermal conductivity, which significantly limits heat dissipation. Consequently, through-glass vias (TGVs) serve as vertical heat conduction paths to prevent device overheating. However, TGVs become critical thermal bottlenecks within glass-based packaging systems due to suboptimal thermal dissipation. With increasing power density and reliability requirements, there is a clear need for a novel approach to enhancing thermal load performance within TGVs.

Innovation:

To address these limitations, researchers at UCLA have developed a thermally enhanced TGV. The technology displays greatly improved heat dissipation within glass interposer substrates. The enhanced TGV preserves the benefits of glass interposers, which include low RF loss, dimensional stability, and cost efficiency. Additionally, the technology can be directly integrated into 3DIC packaging and is compatible with current copper electroplating workflows, enabling seamless adoption into standard manufacturing methods. This novel approach mitigates the thermal bottleneck inherent to TGVs, thereby facilitating accelerated adoption of glass interposers in advanced semiconductor manufacturing.

Potential Applications:

●    3DIC packaging
     ○    High-performance computing 
     ○    Artificial intelligence
●    RF and high-frequency packaging
●    High-power semiconductor devices
     ○    Voltage regulators
     ○    Mixed-signal devices
●    Advanced memory and storage technologies
●    Photonics and optoelectronics

Advantages:

●    Enhanced thermal performance
●    Enhanced thermal reliability
●    Scalable
    ○    Compatible with current copper electroplating workflows
    ○    Easily integratable in 3DIC packaging
●    Glass Interposer
    ○    Low RF loss
    ○    Dimensional stability
    ○    Low cost

Development-To-Date:

First description of the complete invention, simulations proving the concept.

Related Papers:

●    Y. Yang, J. Chien, S. Lyu and T. Wei, "Development of Straight, Small-Diameter, High-Aspect Ratio Copper-Filled Through-Glass Vias (TGV) for High-Density 3D Interconnections," 2025 IEEE 75th Electronic Components and Technology Conference (ECTC), Dallas, TX, USA, 2025, pp. 1036-1042, doi: 10.1109/ECTC51687.2025.00181.

Reference:

UCLA Case No. 2026-156

Lead Inventor:

Tiwei Wei, Assistant Professor, Department of Mechanical and Aerospace Engineering
 

Background:

Advanced semiconductor systems rely on 3D integrated circuits (3DICs) to meet rising demands for higher performance, reduced form factor, and lower latency. Glass interposers and substrates are being adopted for advanced packaging systems due to their low cost and favorable electrical properties. Unlike the current standard of silicon, glass has low thermal conductivity, which significantly limits heat dissipation. To mitigate this limitation, traditional approaches utilize copper-filled through-glass vias (TGVs) to create vertical heat conduction paths. The mismatch in thermal expansion coefficients between copper and glass introduces large thermal stresses, often leading to delamination or cracking. Additionally, copper-filled TGVs are electrically conductive, limiting their use as dedicated thermal conduits in applications requiring electrical isolation. To improve heat dissipation performance of next generation electronic systems, there is a need for a vertical heat path solution that is both thermally conductive and electrically insulating within glass interposers.

Innovation:

Researchers at UCLA have developed a method for the fabrication, insertion, and bonding of material having high thermal conductivity within TGVs, forming electrically insulating vertical thermal conduits.  The design creates continuous heat conduction channels that enable efficient heat dissipation without inducing thermal stress. Compared to conventional glass interposers, the technology achieves a 20x improvement in through-plane thermal conductivity. Additionally, electrical insulation of the vertical thermal conduits enables independent routing of signals and power, increasing design flexibility and preserving signal integrity. The resulting interposer integrates seamlessly into advanced packaging architectures and metal redistribution layers, delivering multifunctional capabilities. It enables direct deployment as a thermally enhanced substrate for high-power integrated circuits, chiplet architectures, and optoelectronic modules. This approach enables superior heat dissipation accompanied by electrical insulation, addressing a key bottleneck in glass-based 3DIC packaging and enabling next-generation semiconductor systems. 

Potential Applications:

●    High-power logic applications
     ○    GPUs, TPUs, AI accelerators
     ○    Improved performance-per-watt
●    3DIC
●    RF, mmWave, wireless modules
●    Optoelectronics and Photonics
     ○    Waveguides 
●    Advanced memory systems

Advantages:

●    Heat Dissipation
●    Electrical Insulation
●    Thermo-mechanical stability
    ○    Reduced mechanical stress from conductivity mismatches 
●    Scalable
    ○    Integratable with glass interposers
    ○    RDLs
●    Maintains glass interposer benefits
    ○    Low RF loss
    ○    Dimensional stability

Development-To-Date:

First description of the complete invention; design has been proven through simulations.

Related Papers:

[1] Y. Yang, J. Chien, S. Lyu and T. Wei, "Development of Straight, Small-Diameter, High-Aspect Ratio Copper-Filled Through-Glass Vias (TGV) for High-Density 3D Interconnections," 2025 IEEE 75th Electronic Components and Technology Conference (ECTC), Dallas, TX, USA, 2025, pp. 1036-1042, doi: 10.1109/ECTC51687.2025.00181.

Reference:

UCLA Case No. 2026-157

Lead Inventor:

Tiwei Wei, Assistant Professor, Department of Mechanical and Aerospace Engineering

Background:

The rapid increase in electric vehicle (EV) adoption creates a critical need for robust and accessible charging infrastructure, particularly in commercial parking spaces and residential garages. Current EV charging infrastructure does not dynamically adjust based on the local grid’s current capacity. Consequently, grid overload may result in decreased charging efficiency, power outages, and additional cost barriers for consumers. Additionally, charging stations tend to be centralized in specific urban locations that may hinder accessibility to certain populations. As EV adoption becomes more widespread and vehicles draw power within localized locations, the power grid experiences heavy stress. During peak hours or power shortages, operators lack the ability to dynamically manage this load, threatening local grid stability. As a result, there is a need for an intelligent charging architecture that enables efficient and dynamic EV charge load management.

Innovation:

To address this growing concern, researchers at UCLA have developed a grid-friendly EV charging architecture designed to manage EV charging loads via variable power control. This invention actively controls and multiplexes current dispersed to multiple vehicles simultaneously, dynamically scaling power delivery based on real-time demand and limitations of the local power grid. When the grid is strained, the system can automatically adjust power distribution instead of shutting off power entirely, satisfying utility constraints while continuing to provide charge. By optimizing power distribution amongst multiple chargers, the system lowers average hardware and energy implementation costs. Additionally, EV charging operators gain flexible and granular control over energy output, enabling the support of more EVs. The invention seamlessly integrates into existing charging networks and stations, eliminating the need to replace current infrastructure and reducing cost barriers for adoption. Thus, this innovation bridges the gap between rising EV infrastructure demands and utility constraints, enabling a scalable solution for next-generation smart charging.

Potential Applications:

●    Commercial parking structures
●    Multi-unit dwellings
●    Commercial fleet depots
●    Charging network retrofitting

Advantages:

●    Reduced CapEx
●    Grid tesiliency & compliance
●    Seamless integration
●    Improved user experience
●    Scalability

Development-To-Date:

First successful demonstration complete.

Related Papers:

•    R. Zahedi, R. L. Sheinberg, S. Narayana Gowda, J. Raj Chadha, K. SedghiSigarchi and R. Gadh, "Phased Planning of Heavy-Duty Electric Vehicle Charging Stations: An Optimization Framework Under Grid Capacity Constraints," in IEEE Access, vol. 14, pp. 18316-18331, 2026, doi: 10.1109/ACCESS.2026.3659818.

•    Ahmadian, A., Sedghisigarchi, K., & Gadh, R. (2024). Empowering Dynamic Active and Reactive Power Control: A Deep Reinforcement Learning Controller for Three-Phase Grid-Connected Electric Vehicles. IEEE Access.

•    Zhang, C., Sheinberg, R., Gowda, S. N., Sherman, M., Ahmadian, A., & Gadh, R. (2023). A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM. Frontiers in Energy Research, 11, 107802.

•    Khaki, B., Chu, C., & Gadh, R. (2019). Hierarchical distributed framework for EV charging scheduling using exchange problem. Applied Energy, 241, 461-471.

•    Xiong, Y., Wang, B., Chu, C., & Gadh, R. (2018). Vehicle Grid Integration for Demand Response with Mixture User Model and Decentralized Optimization. Applied Energy, 231, 481-493.

•    Xiong, Y., Khaki, B., Chu, C., & Gadh, R. (2018). Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation. 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 1-5.

Reference:

UCLA Case No. 2013-146

Patent:

Power control apparatus and methods for electric vehicles (US 9,290,104)

Lead Inventor:

Rajit Gadh
 

Background:

Spatial biology allows researchers to map the coordinates of proteins with subcellular resolution, revolutionizing our understanding of disease. Critical cellular processes are inherently directional, emphasizing the importance of mapping the vector quantities of molecular arrangement. For example, vector-based data is critical for detecting metastasis, evaluating immune cell efficacy, and studying neurobiology. Current spatial biology technologies treat protein expression data as scalar quantities, neglecting the directional orientation or flow of protein distribution within its subcellular environment. As a result, these scalar-based technologies miss critical structural information on how proteins are polarized and trafficked. Currently, there are no bioinformatics tools that convert spatial coordinates into a vector field to model directionality, forcing researchers to rely on manual methods of observing these dynamic states. Thus, there is a need for a bioinformatics solution that models the dynamic, vector-based nature of subcellular protein expression.

Innovation:

Researchers at UCLA have developed a novel bioinformatics tool capable of transforming spatial protein expression data into continuous vector fields. Unlike current methods, this technology enables the calculation of the magnitude and direction of protein distribution. This provides an automatic, quantitative framework to model subcellular orientation, with the potential to transform analytical approaches to spatial biology.

Potential Applications:

●    Oncology 
○    Metastasis Prediction
●    Immunology
○    Synapse Formation
●    Neurobiology
●    Intracellular Transport
●    Developmental Biology
●    Drug Discovery
●    Phenotypic Screening

Advantages:

●    Structural and directional information
○    Produces vector quantities
●    Automation
○    Previously manual & qualitative
○    High-throughput
●    Hardware Agnostic

Reference:

UCLA Case No. 2026-139

Lead Inventor:

Katie Campbell
 

Background:

Cadmium Telluride (CdTe) is a common absorber used for thin-film optoelectronics and photovoltaics, including next-generation solar panels. CdTe accounts for over half of the thin-film photovoltaic market due to its rapid energy payback time. However, current CdTe solar cells are limited by their low open-circuit voltage (Voc), which directly constrains power conversion efficiency (PCE) and potential device performance. This is largely due to the intrinsic Fermi level pinning (FLP) effect caused by interfacial defects between CdTe and metal electrodes, leading to suboptimal contact. While Van der Waals (vdW) contacts have shown promise in overcoming the FLP effect, they are incompatible with the high surface roughness of commercial thin-film CdTe wafers. Attempts to polish the surface can damage the material, further reducing Voc and PCE. Additionally, conventional contacts are deeply integrated into existing manufacturing workflows, making the adoption of alternative methods difficult without scalable processes. Since incremental efficiency improvements lower cost per watt and increase commercial competitiveness, there is a need for a scalable method that enables vdW-contact formation on commercial thin-film CdTe wafers with high surface roughness. 

Innovation:

To address these limitations, researchers at UCLA have developed a selenium-based buffer method that enables the formation of vdW contacts on CdTe wafers with high surface roughness. A selenium layer is utilized as a buffer to protect the substrate from metal-deposition induced damage and vaporization of the layer allows for bond-free semiconductor-metal vdW-contact. Using this approach, gold contacts applied to commercial CdTe wafers delivered meaningfully higher device performance than today’s standard designs, enabling greater power output without sacrificing the fast energy payback that makes CdTe solar technology economically attractive. Importantly, the selenium buffer layer methodology is simple, scalable, and compatible with current manufacturing infrastructure, showcasing its potential for widespread industrial adoption. Beyond photovoltaics, this approach is applicable to other delicate semiconductor applications that experience surface degradation from metallization, including III–V and II–VI compound semiconductors such as GaAs, InP, and HgCdTe. This invention demonstrates a scalable method for integrating vdW contacts onto rough, commercial semiconductor wafers, enabling damage-free metallization for high-performance CdTe solar cells and other surface-sensitive compound semiconductor devices using industry-compatible processes.

Potential Applications:

●    Thin-film CdTe solar cells
●    Next-generation photovoltaics
●    IIIV & II-VI compound semiconductor devices
     ○    GaAs, InP, HgCdTe
●    Optoelectronic devices (with fragile substrates)
     ○    Thin-film photodetectors, LEDs, sensors
●    Rough surface semiconductor devices

Advantages:

●    Reduced interfacial defect states
●    Enhanced open-circuit voltage and power conversion efficiency
●    Compatibility with commercial wafers
●    Minimizes metallization process
●    Scalable
      ○    Simple processing and integration with current manufacturing workflows
●    Short energy payback time
●    Broad applicability
      ○    Applicable to other delicate semiconductor systems

Development-To-Date: First successful demonstration of the invention completed.

Related Papers:

●   Liu, Y., Guo, J., Zhu, E., Liao, L., Lee, S. J., Ding, M., Shakir, I., Gambin, V., Huang, Y., & Duan, X. (2018). Approaching the Schottky-Mott limit in van der Waals metal-semiconductor junctions. Nature, 557(7707), 696–700. https://doi.org/10.1038/s41586-018-0129-8

●   Liu, Y., Huang, Y. & Duan, X. Van der Waals integration before and beyond two-dimensional materials. Nature 567, 323–333 (2019). https://doi.org/10.1038/s41586-019-1013-x 

Reference:

UCLA Case No. 2025-173

Lead Inventors:

Yu Huang, Xiangfeng Duan
 

UCLA researchers in the Department of Atmospheric and Oceanic Sciences have developed a novel and compact device for direct, real-time measurement of solar-induced chlorophyll fluorescence, enabling accurate monitoring of plant photosynthetic activity without complex calibration or spectral retrieval procedures.

Background:

Accurate monitoring of plant photosynthesis is essential for assessing carbon uptake, growth dynamics, and plant responses to heat and water stress. Solar-Induced Chlorophyll Fluorescence (SIF) refers to the emission of photons in the red to far-red spectral region from chlorophyll molecules following excitation by absorbed solar radiation and can serve as a direct proxy for photosynthetic activity. Although SIF has strong potential as a photosynthetic indicator, existing measurement techniques are largely confined to specialized research instruments. These systems are complex, bulky, expensive, require sophisticated spectral retrieval algorithms, and are subject to significant uncertainties arising from atmospheric effects. Thus, there remains an unmet need for a simplified, compact, and cost-effective approach for SIF measurement providing accurate, real-time sensing without reliance on complex numerical retrieval methods.

Innovation:

Dr. Jonas Kuhn and Prof. Jochen Stutz have developed a fundamentally new approach to SIF proximal remote sensing that overcomes the core limitations of existing systems. The proposed device achieves a dramatically reduced form factor and power consumption while simultaneously delivering substantially higher measurement accuracy. The device integrates high spectral resolution with ultra-high contrast performance, enabling direct and absolute quantification of SIF. Consequently, there is no external reliance on complex spectral retrieval algorithms. Notably, the system isolates the SIF signal, suppressing light reflected by a plant canopy, providing unprecedented signal-to-noise performance. Collectively, this technology presents the potential to revolutionize current measurement systems by enabling a low-power, compact, and highly precise platform that can transform plant phenotyping, ecosystem monitoring, and global carbon cycle assessment. This combination of performance, robustness, and efficiency positions the technology as a foundational enabler for next-generation precision agriculture, ecosystem monitoring, and climate research.

Potential Applications:

●    Crop monitoring, selection, irrigation, and fertilization
●    Crop breeding and high-throughput phenotyping
●    Ecosystem and climate change assessment 
●    Carbon flux and productivity monitoring sites
●    Satellite, drone, and airborne remote sensing platform validation

Advantages:

●    Direct, real-time SIF quantification
●    Compact and low-power system design
●    High accuracy and low noise
●    Eliminates complex spectral retrieval models

State of Development:

Working prototype in testing; manuscript pre-print published.

Related Publications:

Direct quantification of solar-induced chlorophyll fluorescence using compact solar-blind optical radiometers

Reference:

UCLA Case No. 2024-183

Lead Inventors:

Jonas Kuhn and Jochen Peter Stutz, Professor, Department of Atmospheric and Oceanic Sciences
 
 

Background:

Conventional disease management systems rely on single-point measurements and discrete interval drug dosing schedules, limiting the ability to track dynamic physiological changes and deliver therapy in a need-responsive manner. Symptom-based and visual monitoring provide only coarse indicators and cannot support precise or timely intervention. Wearable sensor networks have emerged as a promising solution by continuously collecting physiological and environmental data and transmitting it wirelessly to mobile devices and cloud-based platforms for analysis and decision making. Recent advancements have further enabled these systems to deliver therapeutics, including through microneedle-based patches. However, many current systems suffer from limited sensing accuracy, skin irritation, and tissue inflammation, while their bulky and rigid natures can restrict movement and reduce patient compliance. Consequently, there remains a critical need for a compact, highly precise system capable of continuously monitoring target analytes and delivering drugs safely and autonomously, without discomfort or manual intervention.

Innovation:

Professor Aydin Babakhani and his research team have developed a novel closed-loop biosensing and drug-delivery platform that integrates continuous biochemical monitoring with responsive therapeutic release. The implant is capable of reliably transmitting electrochemical signals over extended distances, enabling real-time communication between in vivo sensors and external control systems.  Drug delivery is dynamically regulated based on accurate biosensing of patient-specific biomarkers, with programmable parameters that allow dosing to be precisely tailored to physiological need. A two-stage relay communication architecture supports personalized therapy by linking implant-level sensing with external processing and control, thereby minimizing side effects through tight feedback and biomarker-driven modulation. Collectively, this system represents a transformative advance in disease monitoring and treatment by enabling highly accurate, miniaturized, and autonomous therapeutic control driven directly by the user’s biological signals.  

Potential Applications:

●    Diabetes and metabolic disease management
●    Cancer drug monitoring and dosing
●    Neurological and psychiatric therapies
●    Post-surgical and critical care monitoring 

Advantages:

●    Real-time closed-loop drug delivery
●    Miniaturized and implantable form factor
●    Personalized, biomarker-driven dosing
●    Continuous, high precision biosensing 

State of Development:

First description of complete invention: March 2025

Related Publications:

Mathews, R. P., Habibagahi, I., Jafari Sharemi, H. J., Alderete, J. A., & Danesh, K. (2025). A miniaturized batteryless and wireless biopotential recorder with dynamic bandwidth and data rate update for power optimization. In Proceedings of the 2025 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. DOI:10.1109/EMBC58623.2025.11251531

Mathews, R. P., Jafari Sharemi, H., Habibagahi, I., Jang, J., Ray, A., & Babakhani, A. (2022). Towards a miniaturized, low power, batteryless, and wireless bio-potential sensing node. In Proceedings of the 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. ___). IEEE. DOI:10.1109/BioCAS54905.2022.9948685

H. Lyu, Z. Wang and A. Babakhani, "A UHF/UWB Hybrid RFID Tag With a 51-m Energy-Harvesting Sensitivity for Remote Vital-Sign Monitoring," in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 11, pp. 4886-4895, Nov. 2020, doi: 10.1109/TMTT.2020.3017674.

Reference:

UCLA Case No. 2025-276

Lead Inventor: 

Professor Aydin Babakhani
 

Researchers in the UCLA Department of Electrical and Computer Engineering have developed a magnetically levitated inertial sensor based on a split magnetic dipole architecture. The anchorless design enables high quality factor (Q) gyroscopic sensing with independently tunable sensitivity and bandwidth, providing a robust solution for precision navigation in demanding environments.

Background:

Miniature gyroscopes are foundational components of inertial navigation and stabilization systems used in aerospace, robotics, defense, and autonomous platforms. These applications demand high precision, low drift, long-term stability, and reliable operation in GPS-denied environments such as underground, underwater, or contested regions. Conventional microelectromechanical systems (MEMS) and other anchored resonant gyroscopes are fundamentally limited by anchor-induced damping, thermo-mechanical noise, and structural asymmetries. As device dimensions shrink, anchor losses become increasingly significant relative to the resonator mass, reducing achievable quality factor and degrading bias stability and long-term performance. Magnetically levitated gyroscopes mitigate friction and eliminate anchor-related losses; however, existing levitated architectures typically involve tradeoffs among sensitivity, bandwidth, vibration tolerance, and dynamic range. These coupled constraints limit practical deployment in high-performance navigation systems. Accordingly, there remains a need for a levitated inertial sensor architecture that simultaneously achieves high Q, strong vibration robustness, and independently tunable performance parameters without mechanical anchoring.

Innovation:

Professor Robert Candler and his team have developed a magnetically levitated inertial sensor built on a split magnetic dipole architecture that decouples translational and torsional stiffness control. Implemented as a dual-axis, whole-angle rate gyroscope, the device employs high-speed rotation of a levitated bead to enhance angular sensitivity while eliminating anchor-induced energy loss. The split dipole configuration enables strong translational confinement to improve vibration tolerance and bandwidth, while maintaining compliant torsional stiffness for high angular sensitivity. This independent stiffness tuning allows precise control of scale factor, bandwidth, and dynamic range—addressing a central limitation of prior levitated systems. The architecture achieves a mass–frequency product of greater than 100 g·Hz, among the highest reported for room-temperature levitated systems. In addition, the trap frequency is independent of the bead mass, enabling improved sensitivity without sacrificing bandwidth. When integrated with an accelerometer, the platform can serve as a compact six-degree-of-freedom (6-DOF) inertial measurement unit (IMU). The elimination of mechanical anchors and wear mechanisms supports extended operational lifetimes and improved long-term stability. Collectively, this levitated split-dipole architecture establishes a high-Q, vibration-robust inertial sensing platform with tunable performance characteristics suited for next-generation navigation systems.

Potential Applications:

●    Navigation in GPS denied environments 
     ○    Underground or undersea
     ○    Signal jamming prone environments
●    Defense and military navigation
●    Consumer and industrial motion sensing
●    Space and satellite missions
●    Aviation, marine, and autonomous systems

Advantages:

●    High sensitivity and quality factor
●    Reduced drift, friction, and mechanical wear
●    Tunable bandwidth and dynamic range
●    Compact and scalable design

State of Development:

Public non-confidential disclosure 10/29/25

Related Publications:

1.    US8169114B2 Large gap horizontal field magnetic levitator https://patentimages.storage.googleapis.com/ed/81/5a/09498b6fa59685/US8169114.pdf
2.    Wang et al., “PMN-PT single crystal and Terfenol-D alloy magnetoelectric laminated composites for electromagnetic device applications,” Journal of the Ceramic Society of Japan, 2008

Reference:

UCLA Case No. 2026-191

Lead Inventor:

Robert Candler, Faculty, Department of Electrical and Computer Engineering
 

UCLA researchers in the Department of Electrical and Computer Engineering have developed a liquid-metal-based, nondestructive probing platform for high-density semiconductor die and assembly testing.

Background:

 As semiconductor devices become smaller and more complex, manufacturers face growing challenges in testing chips before they are assembled into advanced packages. Modern chips use extremely small, closely spaced input/output (I/O) pads, making reliable electrical testing at the wafer stage increasingly difficult. Traditional probe technologies require large, dedicated test pads to contact the chip. To accommodate this, designers must add extra structures that consume valuable silicon area, complicate routing, and increase manufacturing cost. These tradeoffs directly impact yield, performance, and overall economics. The challenge is even more critical for multi-die and heterogeneous packages, where multiple chips are integrated into a single high-value module. These systems are expensive to build, cannot be reworked once assembled, and are highly sensitive to defective components. As a result, manufacturers must ensure that only “Known Good Die” (KGD) move forward to assembly. However, existing test approaches can introduce mechanical or electrical stress that risks damaging the die or limiting test accuracy. There is a clear need for a more efficient probing solution—one that enables reliable, fine-pitch electrical access without damaging the chip, while reducing design overhead and simplifying the path to Known Good Die qualification.

Innovation:

To address these limitations, Professor Subu Iyer and his research group have developed a novel liquid-metal probe architecture that enables nondestructive electrical testing of fine-pitch semiconductor dies and assembled devices. The system supports pad pitches below 100 micrometers and into the submicron range by using a conductive liquid interface that eliminates rigid mechanical contact, minimizing stress and preserving surface integrity. A multilayer, high-fanout fabrication process allows dense routing from small pads to standard test hardware, while liquid-metal confinement prevents residue and cross-contamination. The resulting plug-and-play, reconfigurable platform allows chiplets, MEMS, and sensitive devices to be hot-swapped and tested prior to final assembly. This approach significantly improves KGD qualification and reduces yield loss and assembly risk in advanced semiconductor packaging. Together, these capabilities establish a scalable and manufacturing-compatible test solution that lowers cost, improves yield, and enables the next generation of high-density semiconductor integration.

Potential Applications:

●    Known Good Die testing
●    Advanced packaging and chiplet integration
●    Wafer-level probing of fine-pitch I/O
●    MEMS and sensor device testing
●    Biocompatible and soft electronic interfaces

Advantages:

●    Nondestructive, low-stress electrical probing
●    Supports ultra-fine pad pitches
●    High fan-out, multilayer interconnect capability
●    Preserves surface integrity and yield

State of Development:

First description of complete invention: March 2025

Related Publications:

1.    Kim, Mg., Brown, D.K. & Brand, O. Nanofabrication for all-soft and high-density electronic devices based on liquid metal. Nat Commun 11, 1002 (2020). https://doi.org/10.1038/s41467-020-14814-yK.
2.     Amponsah and A. Lal, "Multiple tip nano probe actuators with integrated JFETs," 2012 IEEE 25th International Conference on Micro Electro Mechanical Systems (MEMS), Paris, France, 2012, pp. 1356-1359, doi: 10.1109/MEMSYS.2012.6170418.

Reference:

UCLA Case No. 2026-073

Lead Inventor:

Subu Iyer, Distinguished Professor, Department of Electrical and Computer Engineering
 

Background: 

Microwave ablation (MWA) has emerged as the preferred thermal ablation modality for treating non-surgical patients with primary and metastatic liver malignancies. The clinical success of thermal ablation depends critically on image guidance to ensure sufficient ablation margins while minimizing collateral thermal damage. Traditional image guidance, using modalities such as computed tomography (CT) and ultrasound, is constrained by limited soft tissue contrast and relies on surrogate markers of the ablation zone progression that do not reliably capture true ablation margins. These limitations reduce procedural precision and hinder real-time assessment of thermal dose delivery. Magnetic resonance imaging (MRI)-guided MWA addresses these shortcomings by providing superior soft tissue visualization and enabling real-time, non-invasive temperature monitoring through MR thermometry. This combination enhances targeting accuracy, intra-procedural monitoring, and treatment control, positioning MRI-guided MWA as a highly promising modality for minimally invasive tumor management. 

However, broader clinical adoption of MRI-guided MWA remains limited by electromagnetic interference (EMI) emitted from MWA systems during active operation. EMI contaminates MRI data, obscuring visualization of the microwave antenna, tissue structures, and ablation zone boundaries — increasing the risk of incomplete treatment or collateral thermal damage. Beyond MWA, EMI poses challenges whenever insufficiently shielded powered devices are introduced into the MRI scanner room, constraining the range of MRI-conditional tools and monitoring equipment that can be used during procedures. Existing EMI mitigation approaches rely on specialized hardware modifications (e.g., additional shielding layers or in-line filters) or require suspending energy delivery during image acquisition. These approaches increase system complexity, disrupt therapeutic protocols, and are impractical for routine clinical workflows. Therefore, there is a critical need for a streamlined solution that enables reliable EMI suppression and integrates seamlessly into clinical workflows for MRI-guided interventions.

Innovation: 

To overcome the limitations of existing EMI mitigation approaches, researchers at UCLA have developed a software-based active EMI suppression (AES) framework that restores MRI signal integrity without requiring specialized hardware modifications or workflow disruptions. The framework leverages an unloaded body array coil—an existing clinical MRI system component—to capture raw EMI signatures independently of primary imaging data. This architecture enables seamless integration into existing MRI infrastructure without interfering with image acquisition or procedural workflow. The system characterizes and models the EMI signal on a frame-by-frame basis and adaptively subtracts it from the primary imaging coil data, enabling dynamic, real-time EMI suppression during active microwave ablation.

In controlled testing environments, the technology achieved a 40-fold signal-to-noise ratio (SNR) improvement in phantoms and a 13-fold improvement in vivo, with an EMI suppression rate exceeding 92%. These gains restore image fidelity sufficiently to enable consistent intra-procedural MRI visualization of anatomical details and ablation zone boundaries. The AES framework also preserves thermometric accuracy, maintaining a mean absolute temperature error of <1.4 °C in heated regions and <0.3 °C in non-heated tissue. This level of accuracy supports thermal dose monitoring and helps protect surrounding healthy structures. By eliminating the need for specialized shielding hardware or procedural workarounds, this software-driven AES solution directly addresses key infrastructure and workflow barriers and could facilitate broader clinical adoption of MRI-guided MWA and other MRI-guided interventions affected by EMI. 

Potential Applications:

●    MRI-guided Thermal and Non-Thermal Ablation
     ○    MWA, radiofrequency ablation, laser interstitial thermal therapy, focused ultrasound, cryoablation, pulsed field ablation, histotripsy
●    MRI-Guided Surgical & Robotic Interventions
●    Interventional Oncology & Cardiology 
●    Neuromodulation & Brain Interventions 
●    High-Risk Anatomical Interventions (e.g., proximity to critical structures) 
●    Relaxed MRI Suite Shielding Requirements 
●    Expanded MRI-Conditional Device Integration (e.g., monitors, tools, implants) 
●    Point-of-Care & Low-Field MRI Environments

Advantages:

●    Streamlined Clinical Workflow
     ○    Continuous, real-time visualization
     ○    No pre-training or separate calibration needed
●    Seamless Hardware and Software Integration
     ○    Leverages standard, existing MRI receiver coils — no custom hardware
     ○    Software-only solution, readily integrable into vendor or open-source reconstruction pipelines
●    Superior Intra-procedural Image Quality Preservation
●    Enhanced Patient Safety During Interventional Procedures
●    Reliable Real-time MRI and MR Temperature Monitoring 

Development-To-Date:

First successful demonstration of the invention in controlled gel phantom and in vivo pig liver model

Related Papers:
●   Q. Dai, J. Chiang, S.-F. Shih, W. Zhou, D. S. K. Lu, and H. H. Wu, “Active Electromagnetic Interference Suppression for MRI and Proton Resonance Frequency Shift Thermometry During MRI-Guided Microwave Ablation,” Magnetic Resonance in Medicine (2026): 1–17, https://doi.org/10.1002/mrm.70440.
●    Dai, Qing, et al. “Active Electromagnetic Interference Suppression for Real-Time MR Thermometry During MR-Guided Microwave Ablation.” Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), 2025, Honolulu, Hawai’i, USA, 0677.

Reference:

UCLA Case No. 2026-217

Lead Inventor:

Holden H. Wu
 

Background:

Myositis lung disease (MLD) is a complex autoimmune condition characterized by muscle inflammation and progressive lung fibrosis, resulting in severe respiratory decline. Since MLD is a major driver of mortality in its patient population, the proper management and development of therapeutics rely on accurately tracking lung tissue changes over time for early intervention. Specifically, lung disease occurs in 33-65% of idiopathic inflammatory myopathies (IIM), a diverse group of diseases characterized by autoimmune-mediated inflammation of skeletal muscle. Approximately 80% of the mortality in IIM is associated with some form of lung disease, including MLD, and patients may often be misclassified with idiopathic pulmonary fibrosis (IPF). Currently, high-resolution chest computed tomography (HRCT) remains the clinical standard for the diagnosis and monitoring of MLD, where scans are manually evaluated by expert thoracic radiologists. Due to the reliance on subjective visual assessments and confounding traditional endpoints of MLD, there is an increasing demand for objective, quantitative HRCT. Current first-generation quantitative HRCT technologies rely on taxonomies designed for general fibrotic lung diseases, failing to capture complex patterns specific to myositis. To address this critical limitation, there is a need for a diagnostic and quantitative imaging tool that is specifically designed to quantify MLD for improved patient outcome. 

Innovation:

Researchers at UCLA have developed a diagnostic and quantitative imaging biomarker tool, known as quantitative MLD scoring (QMS), designed for early and objective MLD identification.  QMS utilizes a hybrid artificial intelligence framework that integrates deep machine learning with cognitive AI-based reasoning to establish a novel, MLD-specific taxonomy. This framework aligns computational data with human expertise on MLD disease patterns and clinical outcomes. QMS is capable of MLD quantification, measurement of longitudinal lung tissue change, early and accurate diagnosis of MLD, and the prediction of outcomes such as acute respiratory failure or time to death. This novel framework can significantly improve MLD and lung disease identification in IIM cases, improving patient outcome. Furthermore, QMS establishes an objective reproducible measure that is unconfounded by disease-related factors. By standardizing HRCT scoring across clinical sites, QMS enables reliable pooling of data, overcoming the recruitment and data limitations associated with rare diseases such as IIM. QMS establishes a scalable and disease-specific standard that can accelerate clinical trial timelines, improve risk stratification, and drive the development of novel therapeutics for MLD.

Potential Applications:

●    Clinical Trial Endpoints
     ○    Drug efficacy in MLD
●    Companion Diagnostic (CDx)
●    Clinical Decision Support
●    Predictive Prognostic Modeling
●    Rare Disease Data Standardization

Advantages:

●    Disease Specificity
     ○    Proprietary taxonomy
●    Unconfounded and Objective Data
○    Eliminates human subjectivity
●    Accelerated Trial Timelines
●    Precision
     ○    Capable of detecting microscopic longitudinal changes not possible by human experts

Development-To-Date: First description of complete invention (oral or written)

Related Papers:

●   Brown, Matthew, et al. "Quantitative CT and Artificial Intelligence in Myositis-associated Interstitial Lung Disease: A Review." Journal of Thoracic Imaging, 2024,  journals.lww.com/thoracicimaging/fulltext/9900/quantitative_ct_and_artificial_intelligence_in.206.aspx.

Related Technologies:

● Automatic Diagnosis of Idiopathic Pulmonary Fibrosis (IPF) Using High-Resolution Computed Tomography (HRCT) (Case No. 2020-387)
● Early Prediction of Progression in Idiopathic Pulmonary Fibrosis Using a Single Time Point HRCT Scan (Case No. 2019-731)
● Automated Image System for Scoring Changes in Quantitative Interstitial Lung Disease (Case No. 2013-078)
● Issued Patent: US-9582880-B2: Automated image system for scoring changes in quantitative interstitial lung disease

Reference:

UCLA Case No. 2026-189

Lead Inventors:

Jonathan Goldin, Grace Hyun Kim, Sangmee Bae, Matthew Brown
 

Background:

Scalable superconducting quantum processors require low-loss, high-fidelity interconnects for signal routing between qubits, chips, and distributed cryogenic modules. Conventional microwave interconnects are reciprocal, allowing back-propagating noise, crosstalk, and spurious excitations to travel between subsystems, degrading qubit coherence, entanglement fidelity, and gate performance. Existing nonreciprocal solutions, such as ferrite-based circulators, insulators, and active microwave switching networks, rely on magnetic biasing, exhibit insertion loss, and are bulky and difficult to integrate within cryogenic and magnetically-sensitive superconducting environments. Further, their size, power requirements, and limited scalability make them unsuitable for densely integrated quantum architectures. Thus, there is an unmet need for a compact, low-loss, fully superconducting, and intrinsically directional interconnect that can provide on-chip and inter-module isolation while preserving quantum coherence in large-scale superconducting quantum systems. 

Innovation:

Professor Pri Narang and her research team have developed a superconducting diode (SD)-based coupler that enables intrinsically nonreciprocal microwave transmission between qubits, chips, and cryostat-separated modules. Implemented as a fully superconducting, passive interconnect, the SD enables low forward impedance and high transmission to preserve quantum coherence and support high-fidelity state transfer and entanglement distribution. Its high reverse impedance simultaneously suppresses back-propagating noise, crosstalk, and spurious excitations, protecting idle qubits and reducing correlated errors. Unlike ferrite-based or actively biased nonreciprocal components, the proposed design is compact, magnet-free, low-loss, and directly compatible with scalable superconducting circuit integration, representing a significant advancement in directional quantum interconnects.

Potential Applications:

●    Superconducting quantum processors 
●    Modular and distributed quantum computing
●    Qubit and chip interconnects
●    Cryogenic quantum networking
●    Quantum measurement and control systems

Advantages:

●    No external isolators or circulators
●    Low-loss superconducting operation
●    High fidelity and entanglement success probability
●    Compact, chip-integrated form factor
●    Passive, bias-free design
●    Scalable quantum system integration 

State of Development:

First description of complete invention: August 2025

Related Publications:

Dirnegger, Nicolas, et al. &quot;Nonreciprocal Quantum Information Processing with Superconducting Diodes in Circuit Quantum Electrodynamics.&quot; arXiv, 25 Nov. 2025, https://doi.org/10.48550/arXiv.2511.20758.

Reference:

UCLA Case No. 2026-078

Lead Inventor:

Professor Prineha Narang
 

Background:

Superconducting quantum processors can perform complex calculations orders of magnitude quicker than classical computers. Current superconducting quantum processors rely on dispersive readouts to measure a superconducting qubit’s state. To ensure high-fidelity measurement, traditional systems utilize ferrite-based components that limit the scalability of superconducting quantum computers. While ferrite components are necessary to block noise, they come with significant drawbacks in scalability and signal quality. These components are bulky, occupying space in the chandelier and severely limiting the density of signal lines. In addition, ferrites introduce insertion loss, significantly degrading the signal-to-noise ratio and reducing signal fidelity. Ferrite-based components require strong magnetic fields that may be harmful to hardware, necessitating complex shielding to protect superconducting qubits. To overcome these limitations and improve quantum signal fidelity, there is a need for a readout solution that is compact, lossless, and fully integrated on-chip.

Innovation:

Researchers at UCLA have developed a passive, on-chip superconducting diode readout chain. The readout chain contains a superconducting qubit, a dispersive readout resonator, and a superconducting diode placed before the cryogenic amplifier. The architecture enforces unidirectional signal flow by maximizing forward transmission while blocking reverse path noise to prevent signal backpropagation. The device exhibits a forward-to-reverse transmission of over 20dB at the readout frequency, matching isolation levels of commercial ferrite circulators at a fraction of the insertion loss. This ensures high-fidelity readout by maintaining a high signal-to-noise ratio. Additionally, this invention offers flexible implementation, utilizing either Josephson diodes or bulk superconductor configurations, enabling a scalable fabrication process compatible with existing superconducting foundry capabilities. This novel architecture resolves critical bottlenecks in existing quantum computing solutions, paving the way for the next generation of high-fidelity superconducting quantum processors.

Potential Applications:

●    Superconducting Quantum Processors
●    Qubit Measurement
●    Cryogenic Sensor Arrays
●    Integrated Quantum Circuitry

Advantages:

●    Scalable
     ○    Compatible with standard foundries
●    Compact
●    No Magnetic Interference
●    Enhanced Readout Fidelity
     ○    Improved signal-to-noise ratio
●    Passive

Development-To-Date:

First description of the complete invention.

Related Papers:

Dirnegger, Nicolas, et al. &quot;Nonreciprocal Quantum Information Processing with Superconducting Diodes in Circuit Quantum Electrodynamics.&quot; arXiv, 25 Nov. 2025, https://doi.org/10.48550/arXiv.2511.20758 

Reference:

UCLA Case No. 2026-079

Lead Inventor:

Professor Prineha Narang
 

Background:

Metal thin films deposited on inert polymer substrates are critical components in wearable electronics, sensors, and flexible electrodes. To create high purity and uniform polymer-metal thin-film composites, chemical vapor deposition (CVD) and physical vapor deposition (PVD) are utilized. While capable of producing high-quality films, these techniques typically require vacuum systems, elevated temperatures, and capital-intensive equipment, limiting compatibility with flexible substrates and increasing manufacturing costs. Electroless plating has shown promise as a solution-based technique that enables metal film deposition on chemically inert substrates via redox reactions, without the need for an external current. Despite this, electroless plating has significant limitations. Sensitization, a critical step in deposition, relies on acidic tin-based solutions which raise environmental and health concerns, an issue of particular relevance in wearable electronics. In addition, activation, a pre-treatment process in electroless plating, utilizes increasingly costly noble metals, which poses economic limitations for large-scale implementation. Thus, there is a need for an alternative electroless plating process that mitigates health risks while improving scalability and cost-effectiveness.

Innovation:

Researchers at UCLA have developed an electroless plating solution capable of forming metal thin films on chemically inert substrates without the use of traditional sensitization and activation techniques. This formulation enables copper deposition on PI and PET films without the need for toxic tin sensitization or noble metal activation reagents. This methodology significantly simplifies the production of polymer-metal thin-film composites and compresses the processing timeline, as functional films form within minutes. The system’s wide applicability is driven by a facile salt metathesis protocol, which allows for the deposition of many metals using standard solutions. The system can accommodate most transition metal ions used in industrial thin films while maintaining conventional film formation characteristics. Furthermore, the system minimizes the deleterious surface wetting issues found in aqueous solutions, enabling uniform plating on hydrophobic plastics and glass. The technology provides precise control over surface morphology, ensuring that prolonged plating time leads to smooth and uniform surfaces. In conclusion, this methodology provides a precise and widely applicable framework that improves the production of polymer-metal thin-film composites by simplifying the process and improving scalability.

Potential Applications:

●    Flexible Electronics and Circuits
     ○    Wearable biosensors
●    EMI/RFI Shielding
●    Advanced Sensors
●    Microfluidics
●    Aerospace and defense 
●    Automotive materials
●    Any application where thin layers of copper are desired

Advantages:

●    Simplified pre-treatment
     ○    Cost effectiveness
●    Non-Toxic Reagents
●    Rapid processing
●    Scalability
●    Controllable thickness of applied metal films

Development-To-Date:

Compound developed; validation in progress.

Related Papers:

●    Nava, Matthew., et al. “Metal–Ligand Cooperativity Enables Zero-Valent Metal Transfer.” Chemical Science, vol. 16, 2025, pp. 3888–3894. Royal Society of Chemistry, https://doi.org/10.1039/D4SC07938H

Reference:

UCLA Case No. 2026-211

Lead Inventors:

Matthew Nava, Oliver Garcia
 

Background:

Rapid and accessible cardiac biomarker testing is essential for timely diagnosis and risk assessment of myocardial infarction and heart failure, which together account for more than one-third of cardiovascular mortality worldwide. Despite their severity, current laboratory and point-of-care testing systems remain limited by long turnaround times, narrow dynamic ranges, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Conventional benchtop analyzers typically require separate cartridges, specialized reagents, and relatively large blood volumes, often producing results only after several hours. Point-of-care testing offers improved accessibility but still faces challenges including limited sensitivity for key biomarkers such as cardiac troponin, restricted multiplexing capability, and instrument formats that remain relatively bulky rather than handheld. These limitations in the current state of the art hinder effective early detection of cardiac disease, ultimately increasing the burden on healthcare systems. Thus, there remains an unmet need for a cost-effective and simplified platform capable of rapid, multiplexed, and highly sensitive point-of-care diagnostics for cardiovascular diseases.

Innovation:

To address these limitations, Professor Aydogan Ozcan and his research team have developed a novel deep-learning-enhanced dual-mode multiplexed vertical flow array (xVFA). This device is integrated with a portable optical reader and a neural network-based quantification pipeline. The system achieves a dynamic detection range spanning approximately six orders of magnitude, enabling simultaneous measurement of both low- and high-abundance cardiac biomarkers with sub-pg/mL to sub-ng/mL sensitivity. The platform provides rapid assay turnaround (~23 minutes) while maintaining high quantitative accuracy through automated neural network–based signal analysis. By combining high sensitivity, multiplexing capability, and automated data processing within a compact and cost-effective optical sensor architecture, the dual-mode xVFA enables fast and reliable cardiovascular diagnostics at the point of care. The system simultaneously quantifies multiple biomarkers within a single disposable cartridge, and experimental validation demonstrates that multiplexed detection of key cardiac markers does not compromise analytical specificity. This architecture significantly advances point-of-care diagnostics by enabling comprehensive and scalable cardiovascular biomarker analysis in diverse healthcare environments including hospitals, emergency departments, community clinics, and long-term care facilities.

Short summary:
A dual-mode vertical flow assay integrates colorimetric and chemiluminescent sensing modalities, a portable reader, and neural network analysis for rapid, multiplexed detection of cardiac biomarkers at the point of care.

Potential Applications:

●    Point-of-care cardiovascular diagnostics
●    Myocardial infarction detection and triage
●    Heart failure risk stratification
●    Treatment monitoring and prognosis assessment
●    Multiplexed biomarker testing in decentralized and emerging healthcare settings
●    Congenital disease detection and monitoring

Advantages:

●    Multiplexed detection of multiple cardiac biomarkers in a single assay
     ○    Creatine Kinase-MB (CK-MB)
     ○    Cardiac Troponin I (cTnI)
     ○    N-terminal pro-B-type natriuretic peptide (NT-proBNP)
●    Six-order-of-magnitude dynamic detection range
●    Sub-pg/mL to sub-ng/mL analytical sensitivity
●    Rapid turnaround with automated neural-network analysis
●    Compact and portable point-of-care platform for rural or emerging environments 
●    Reduced reagent consumption and simplified workflow

Status of Development:

First successful demonstration March 2025

Related Technologies and Publications:

• Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing (Case No. 2025-128)
• Han, G.-R.; Goncharov, A.; Eryilmaz, M.; Joung, H.-A.; Ghosh, R.; Yim, G.; Chang, N.; Kim, M.; Ngo, K.; Veszpremi, M.; Liao, K.; Garner, O. B.; Di Carlo, D.; Ozcan, A. Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification. ACS Nano 2024, 18, 27933–27948. https://arxiv.org/pdf/2402.11195 
• Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification (Case No. 2024-179)

Reference:

UCLA Case No. 2026-178

Lead Inventors:

Aydogan Ozcan, Chancellor’s Professor, Department of Electrical and Computer Engineering and Bioengineering; Dino Di Carlo, Bioengineering Department Chair
 

Background:

Glaucoma is a heterogeneous, progressive eye disease that is a leading cause of irreversible blindness. Despite the emergence of improved diagnostic tools, treatment decisions for glaucoma remain largely empirical and non-standardized. Clinicians often initiate treatment with medication and may later escalate to combination therapy, laser, or surgery if first-line treatments prove unresponsive. While these methods aim to lower the primary modifiable risk factor for glaucoma progression, there are no validated methods for categorizing patients into subgroups that tailor treatment based on individual characteristics. As a result, patients often endure cycles of medication changes, ineffective therapy, or invasive surgical intervention. This trial-and-error approach contributes to added costs, poor prognosis, and increased burden to all parties involved. There remains an unmet need for a personalized approach that can stratify patients and enable individualized treatment pathways, ultimately improving outcomes, reducing complication risk, and lowering the overall burden on the healthcare system.

Innovation:

To address these limitations, researchers at UCLA have developed a patient stratification machine learning algorithm designed to predict treatment response in patients with open-angle glaucoma. The algorithm utilizes routinely available clinical data to create a patient stratification score at the time of diagnosis, enabling clinicians to rapidly match patients with an optimal first-line treatment. This approach shifts glaucoma care from an empirical, trial-and-error methodology to a personalized regimen, reducing the likelihood for costly medication changes and ineffective cycles of therapy. Early implementation of optimal glaucoma treatment approaches significantly improves long-term medication adherence and reduces the likelihood of disease progression. Additionally, the technology may be directly integrated into existing clinical decision systems, deployable as a customizable, standalone web application. By transforming standard clinical data into actionable treatment insights, this innovation provides a scalable solution that modernizes glaucoma management.

Potential Applications:

●    Point-of-Care cinical decision support
●    Electronic health record integration
●    Clinical trial enrichment
●    Telehealth and remote monitoring
●    Platform technology for a diverse array of ophthalmologic applications 

Advantages:

●    Data-driven methodology
●    Cost reduction
●    Improved long-term outcomes
●    Interpretable
●    Flexible deployment

Development-To-Date:

First successful demonstration of the invention completed.

Related Papers:

●    Rahmani, E., et al. (2025). Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response. medRxiv, 10.1101/2025.08.28.25334696v1.
●    Rahmani, E., et al. (2024). Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans. Nature Biomedical Engineering (Indexed in PubMed: 39354052)

Reference:

UCLA Case No. 2026-122

Lead Inventors:  

Elior Rahmani, Arush Ramteke
 

UCLA researchers have developed a machine learning-based clinical decision support tool that predicts patient-specific cognitive impairments following traumatic brain injury (TBI) using neuroimaging and multimodal clinical data. The software leverages MRI-derived structural brain information to generate individualized diagnostic and prognostic insights, enabling clinicians to anticipate outcomes such as depression, PTSD, and cognitive decline within months post-injury.

Background:

Traumatic brain injury (TBI) is a leading cause of long-term neurological disability, affecting over 5.3 million individuals in the United States and millions more globally each year. TBIs range in severity from mild concussions to severe brain damage, often resulting in persistent cognitive, emotional, and behavioral impairments. These may include deficits in memory, attention, executive function, mood regulation, and increased susceptibility to conditions such as depression and post-traumatic stress disorder (PTSD).

Despite the high prevalence and heterogeneity of TBI outcomes, current clinical workflows lack precise tools to predict individualized cognitive trajectories. Standard treatment protocols often apply generalized care pathways, such as limited follow-up or uniform discharge recommendations, which fail to account for patient-specific brain structure, injury patterns, and resilience variability. This results in suboptimal allocation of medical resources and missed opportunities for early intervention in high-risk patients. There is a critical unmet need for tools that can bridge the gap between observed neural damage and downstream cognitive outcomes to support personalized care and optimized patient outcome.

Innovation:

UCLA researchers have developed a novel, software-based platform that integrates neuroimaging and machine learning to directly link structural brain damage with predicted cognitive impairments on a per-patient basis. The tool uses MRI data as its primary input and applies advanced predictive modeling to forecast specific neuropsychiatric and cognitive outcomes, including depression, PTSD, and cognitive dysfunction, up to three months post-injury. This approach is grounded in a first-of-its-kind scientific framework that quantitatively correlates neural injury patterns with cognitive and behavioral outcomes. The model captures inter-patient variability, demonstrating that individuals with similar injuries and medical histories can experience significantly different cognitive trajectories—ranging from high resilience to severe impairment.

In addition to MRI data, the platform is designed to incorporate multimodal inputs such as blood-based biomarkers and cognitive assessment scores (e.g., questionnaires), enhancing predictive accuracy and clinical flexibility. The system is adaptable to a wide range of neurological and psychological conditions beyond mild TBI, including moderate to severe (e.g., coma patients), and conditions such as Alzheimer’s Disease and stroke. By automating the interpretation of complex neuroimaging data and translating it into clinically actionable predictions, this tool enables a shift toward precision medicine in neurotrauma care. It supports early identification of high-risk patients and facilitates targeted interventions, including referrals, monitoring, and treatment planning.

Potential Applications:

●    Clinical management of traumatic brain injury based on severity
●    Mild TBI (concussion)
●    Moderate to severe (e.g., coma patients)    
●    Military and veteran healthcare (e.g., PTSD and blast injuries)
●    Sports medicine and concussion management (return-to-play protocols)
●    Neurology and psychiatry decision support systems
●    Rehabilitation planning and cognitive therapy allocation
●    Extension to other neurological and psychiatric disorders
●    Alzheimer’s Disease
●    Stroke

Advantages: 

●    Patient-specific prediction of cognitive outcomes based on brain structure
●    Early prognostic capability (up to 3 months post-injury)
●    Integration of multimodal data (MRI, biomarkers, cognitive assessments)
●    Enables personalized treatment and resource allocation
●    Identifies high-risk patients who may otherwise be overlooked
●    Flexible framework adaptable to multiple neurological conditions
●    Improves clinical decision-making and care efficiency

State of Development:

First description of the complete invention June 2025. Prototype software developed and validated using clinical datasets, including mild TBI patient cohorts. Model development and validation are documented in dissertation research, with demonstrated predictive capability linking neural damage to cognitive outcomes.

Related Publications and Patents:

https://escholarship.org/content/qt8q04g03w/qt8q04g03w.pdf

Reference:

UCLA Case No. 2026-251

Inventors:

Sonya Ashikyan, Martin Monti, Jeffrey Chiang
 

UCLA researchers in the Department of Computer Science have developed a next-generation universal pathogen diagnostic platform combining scalable genomic sequencing, simplified laboratory workflows, and integrated logistics infrastructure to enable rapid, low-cost, high-throughput infectious disease detection.

Background:

The rise of pathogen-specific therapies for diseases such as Hantavirus, COVID-19, and influenza has increased demand for accurate, high-throughput diagnostics capable of identifying a broad range of infectious agents. Current multiplexed PCR tests are limited by cost, throughput, and reliance on healthcare worker-collected samples, restricting their use in large-scale screening. Existing sequencing-based approaches, including nanopore and capture enrichment methods, face constraints in capacity, turnaround time, and infrastructure requirements, often requiring expensive and specialized laboratory equipment. As a result, these limitations hinder rapid outbreak response and real-time public health surveillance at scale. There remains an unmet need for a low-cost, accurate, scalable diagnostic platform that can detect diverse pathogens in routine clinical and community settings. 

Innovation:

Professor Eleazar Eskin and his research team have developed a scalable genomic diagnostic platform that combines simplified sequencing workflows with integrated logistics infrastructure for high-throughput pathogen detection. The system enables detection of hundreds of pathogens in a single assay while reducing equipment costs by several orders of magnitude. It uses a minimal hands-on workflow, interoperable self-collection kits, and digital tracking infrastructure to support rapid sample collection and processing. Samples can be delivered within two hours and returned with results in approximately six hours. The platform is further designed to be compatible with existing laboratory infrastructure, enabling immediate adoption without major workflow disruption. This integrated system enables population-scale infectious disease testing with significantly expanded diagnostic breadth and reduced cost and complexity compared to existing methods.

Potential Applications:

●    Primary care and outpatient clinics
●    Urgent care centers and pharmacy-based testing labs
●    Large clinical laboratories and integrated health systems 
●    Self-collection testing via distributed vending systems
●    Population-scale screening and surveillance programs
●    Digital health platforms with smartphone-enabled reporting and tracking

Advantages:
 
●    Scalable and infrastructure-compatible 
●    Low-cost testing
●    High-throughput and multiplexed processing
●    Minimal laboratory equipment required 

State of Development:

Logistics and IT infrastructure have been developed and validated for scalable COVID-19 diagnostic testing and are now being expanded to broader respiratory disease applications. Prototype systems and three tiers of deployable deliverables are planned for further validation and rollout.

Related Publications and Technologies:

Swabseq Agnostic Diagnostic Platform (Case No. 2023-293)

Reference:

UCLA Case No. 2026-121

Lead Inventor:

Eleazar Eskin, Faculty, Department of Computer Science
 

Background:

Magnetic resonance imaging (MRI) is a versatile, non-invasive medical imaging tool that uses a magnetic field and computer-generated radio waves to create detailed images of organs and tissues in the human body, playing a critical role in diagnostic medicine. Multi-dimensional MRI builds on these capabilities by enabling the acquisition and analysis of data across multiple dimensions, offering a richer characterization of biological structure and function in real time. Despite its high clinical value and growing research interest, broader adoption of multi-dimensional MRI remains limited by long acquisition times which can disrupt clinical workflow, reduce patient tolerance, and constrain wider use. While existing acceleration strategies reduce scan time, higher acceleration rates come at the expense of image quality and spatiotemporal fidelity, hindering the ability to obtain reliable, information-rich images needed for advanced clinical and research applications. There is therefore a clear unmet need for improved MRI methods that enable faster multi-dimensional imaging, while maintaining high image quality and reliable spatiotemporal resolution.

Innovation:

To address these limitations, UCLA researchers have developed a physics-guided, self-supervised deep learning framework for reconstructing highly accelerated multi-dimensional MRI data with high image quality and spatiotemporal fidelity. By combining data-driven reconstruction with physics-based constraints, the technology is designed to preserve spatial detail and temporal consistency even at high acceleration rates, where existing methods struggle. This innovation enables faster imaging without sacrificing the quality of information needed for advanced clinical and research applications. The self-supervised nature of the framework makes it especially well-suited for multi-dimensional MRI, where fully sampled ground-truth data are often difficult to obtain. Ultimately, this novel innovation is adaptable across a wide range of multi-dimensional MRI applications and enables faster imaging while maintaining high image quality and spatiotemporal fidelity, making it a valuable tool for improving diagnostic imaging and easing clinical workflow.

Potential Applications:

-    Cardiovascular MRI applications – Enables highly accelerated multi-dimensional cardiovascular imaging, including 4D MUSIC and 4D flow MRI, with preserved image quality and spatiotemporal fidelity.
-    Quantitative and contrast-enhanced MRI – Supports advanced MRI applications such as proton density fat fraction (PDFF) mapping, T1 mapping, T2 mapping, T2*/R2* imaging, and
contrast-enhanced multi-dimensional imaging.
 
-    Software integration for existing clinical MRI scanners – Can be deployed as a reconstruction add-on to enhance image quality and support faster multi-dimensional MRI workflows.
-    Broad multi-dimensional MRI applications – Can be adapted for a wide range of advanced
multi-dimensional MRI applications while enabling faster acquisition without compromising image quality or spatiotemporal fidelity.

Advantages:

-    Enables highly accelerated multi-dimensional MRI reconstruction
-    Maintains high image quality even at high acceleration rates
-    Preserves spatiotemporal fidelity in dynamic MRI dataset
-    Uses self-supervised learning from under sampled MRI data
-    Eliminates the need for fully sampled ground-truth training datasets
-    Can be adapted across a broad range of multi-dimensional MRI applications
-    Supports faster MRI acquisition without sacrificing important diagnostic information

State of Development:

The inventors have developed a physics-guided, self-supervised deep learning reconstruction framework for highly accelerated multi-dimensional MRI and demonstrated its performance in representative cardiovascular MRI applications, including 4D MUSIC and 4D flow MRI. The technology has been trained and evaluated on prospectively acquired under sampled datasets and has shown improved image quality, reduced artifacts, and preserved spatiotemporal fidelity at high acceleration rates.

Related Papers:

-    Finn JP, Shih SF, Nguyen KL, Bedayat A, Yoshida T, Jin N, Han F, Zhong X. Four-dimensional, ferumoxytol-enhanced MUSIC (multi-phase steady-state imaging with contrast) in a single breath-hold: Technical feasibility in structural heart disease. Proc Intl Soc Mag Reson Med 2025;33:402. Honolulu, Hawaii.

Reference:

UCLA Case No. 2026-164

Lead Inventors:

Xiaodong Zhong, Department of Radiological Sciences; John Finn, Department of Radiological Sciences
 

Background:

Following abdominal surgeries and related medical procedures, it is essential to properly secure the abdomen to promote healing, manage medical attachments, and provide the patient with pain relief. The current standard of care utilizes traditional abdominal binders that are not designed to naturally accommodate these attachments. As a result, these standard garments require specialized equipment or must be retrofitted around drains, sutures, or pouches. This process is inefficient and may compromise the supportive functions of the binder. Thus, there is a critical need for a versatile abdominal binder that accommodates post-operative medical devices without compromising abdominal support. 

Innovation:

To address this unmet clinical need, Dr. Esau Baqi has developed a specialized post-operative medical garment designed to accommodate surgical drains, sutures, and ostomy pouches while providing secure abdominal support. Unlike conventional abdominal binders that often require modification or supplemental equipment, this garment incorporates dedicated features that seamlessly integrate with common post-surgical devices. By eliminating the need for improvised alterations and reducing time spent sourcing specialized accessories, the technology streamlines post-operative care workflows and improves consistency in patient management. The garment is designed to enhance patient comfort, support healing, and facilitate mobility while reducing operational burdens on healthcare providers. Overall, this innovation has the potential to improve post-surgical outcomes, standardize abdominal support across diverse patient populations, and increase efficiency in both inpatient and outpatient care settings.  

Potential Applications: 

• Post-operative recovery 
• Medical attachment management 
• No manual alterations needed to incorporate various drains, sutures, and ostomy pouches 

Advantages: 

• Clinical care standardization 
• Clinical efficiency 
• Reduces time wasted 
• Versatility 

Development-To-Date:

First written description of complete invention; patent application filed.  

Reference:

UCLA Case No. 2026-022 

Lead Inventor:

Esau Baqi, Department of Rehabilitation Services