Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of brain development, providing an exceptional tool for studying the complexities of biology. Among these, cortical organoids, comprising in part of neurons, have been instrumental in providing early insights into brain formation, function, and pathology. Functional characteristics of cortical organoids, such as cellular morphology and electrophysiology, provide physiological insight into cellular states and are crucial for understanding the roles of cell types within their specific niches. And while progress has been made studying engineered neuronal systems, decoding the functional properties of neuronal networks and their role in producing behaviors depends in part on recognizing neuronal cell types, their general locations within the brain, and how they connect.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter "organoid") have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Modern methods of interfacing with organoids involve any combination of encoding information, decoding information, or perturbing the underlying dynamics through various timescales of plasticity. Our knowledge of biological learning rules has not yet translated to reliable methods for consistently training neural tissue in goal-directed ways. In vivo training methods commonly exploit principles of reinforcement learning and Hebbian learning to modify biological networks. However, in vitro training has not seen comparable success, and often cannot utilize the underlying, multi-regional circuits enabling dopaminergic learning. Successfully harnessing in vitro learning methods and systems could uniquely reveal fundamental mesoscale processing and learning principles. This may have profound implications, from developing targeted stimulation protocols for therapeutic interventions to creating energy-efficient bio-electronic systems.

A revolutionary software that integrates with surgical microscopes to accurately locate the trabecular meshwork (TM), enhancing the safety and efficiency of glaucoma surgeries.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter “organoid”) have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Despite their potential, organoid experiments present several challenges. Organoids require a rigorous, months-long developmental process, demanding substantial resources and meticulous care to yield valuable data on aspects of biology such as neural unit electrophysiology, cytoarchitecture, and transcriptional regulation. Traditionally the data has been difficult to collect on a more frequent and consistent basis, which limits the breadth and depth of modern organoid biology. Generating and measuring organoids depend on media manipulations, imaging, and electrophysiological measurements. Historically these are labor- and skill-intensive processes which can increase risks associated with known human error and contamination.

Researchers at Vanderbilt University and the University of California, Davis have developed MCNC software that significantly compresses large AI models while maintaining their performance using a novel manifold-constrained optimization approach.

An innovative system designed to enhance rehabilitation therapy for neurological conditions through comprehensive, computer-based solutions.

An innovative online method for personalized tinnitus treatment through customized therapeutic sounds and habituation exercises.

Researchers at the University of California Davis have developed a technology that enables the provable editing of DNNs (deep neural networks) to meet specified safety criteria without altering their architecture.

Researchers at the University of California, Davis have developed a technology that introduces a novel approach to hardware security using photonic physically unclonable functions for true random number generation and biometric ID.

Researchers at the University of California, Davis have developed a machine learning (ML) malware detector based on a randomization technique to prevent cyberattacks on computer systems and networks.

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Researchers at the University of California, Davis have developed a haptic interface designed to aid visually impaired individuals in navigating their environment using their portable electronic devices.

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A revolutionary approach to sensor data processing that leverages bio-inspired computing for intelligent sensing.

This technology introduces a novel end-to-end method for automatic data annotation and generation based on robust temporal causality among data streams, enhancing machine learning model accuracy and adaptability.

A revolutionary method utilizing machine learning to derive systems biology models from experimental data to improve drug discovery and development.

This technology introduces a novel method for automatic data annotation and generation, enhancing machine learning model accuracy and adaptability

A software infrastructure designed to rapidly develop and test aligned conversational AI assistants for specific tasks.

Metadata is used to summarize basic information about data that can make tracking and working with specific data easier. Today’s communication systems, like WhatsApp, iMessage, and Signal, use end-to-end encryption to protect message contents. Such communication systems do not hide metadata, which is the data providing information about one or more aspects of such contents, like messages. Such metadata includes information about who communicates with whom, when, and how much, and is generally visible to systems and network observers. As a result, cyber risk associated with metadata leakage and traffic analysis remains a significant attack vector in such modern communication systems. Previous attempts to address this risk have been generally seen as not secure or prohibitively expensive, for example, by imposing inflexible bandwidth restrictions and cumbersome synchronous schedules globally, which cripples performance. Moreover, prior approaches relied on distributed trust for security, which is largely incompatible with conventional organizations hosting or using such apps.

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Many people have learned to appreciate the advent of GPS based navigational applications in our daily lives through the use of street level navigation, and many more loathe the same applications when using them to navigate established public transportation systems. Many of these travelers become confused and frustrated when attempting to understand and act on the directions given to them by such existing applications that primarily focus on large-scale street navigation, especially if the user has a visual or cognitive impairment. Several existing applications will not even attempt to aid someone in the navigation of say, a metro, train or bus station, and instead simply inform the user of the label of the route that the application intends the user to take. Without any small-scale directions many people find themselves struggling to figure out what platform or boarding zone they need to use to get on their preferred method of transportation, as well as how to get to these platforms and boarding zones in the first place. These transit hubs, plazas, malls, and the like have long been a pain in the side of developers and users alike when it comes to navigation. Innovation has long been overdue in this space concerning small scale transit plaza navigation, with major players holding large market shares in navigation not even attempting to address this longstanding problem. The only existing application to offer indoor navigation offers very limited as well as inconsistent functionality including only two-dimensional indoor mapping, due to manually uploaded floor plans that are only available in the first place from partnering locations. This has continued to be an issue due to a lack of adoption by existing locations, as each location is required to draw out their floor plan on an antiquated image file and submit it for approval. Solving this problem would ease a large amount of stress for those navigating in areas they are not familiar with, as well as saving time that could possibly make the difference between a missed train and a nearly missed train.

Efficiently analyzing large volumes of information, as found in streaming data and big data applications, requires accurate cardinality estimates. This invention is capable of more accurately estimating cardinalities while using little memory and compute, as a result, speeding up query evaluation by as much as 50%.

The Mandt lab introduces a novel approach to neural image compression, significantly reducing decoding complexity while maintaining competitive rate-distortion performance.

Polymer electrolyte membrane (PEM) electrolyzers have received increasing attention for renewable hydrogen production through water splitting. In order to develop such electrolyzers, it is necessary to understand and model the flow of liquids, gases, and ions through the PEM. An advancedmulti-dimensional multi-physics model is established for PEM electrolyzer to describe the two-phase flow, electron/proton transfer, mass transport, and water electrolysis kinetics.