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Applications of generative modeling and deep learning in physics-based systems have traditionally focused on building emulators - computationally inexpensive approximations of input-to-output maps. However, the remarkable flexibility of data-driven architectures opens opportunities to broaden their scope to include…
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Large Language Models are increasingly deployed in applications that require reasoning over long and complex context, such as extended documents, multi-turn interactions, retrieved evidence, and multimodal inputs. While these capabilities make LLMs more powerful, they also introduce new and underexplored safety risks.…
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This talk presents two recent efforts where machine learning addresses fundamental challenges in wearable robot modeling and control. The first contribution addresses the robot side: a hybrid Neural ODE framework for modeling artificial muscle dynamics that embeds physical structure into a learned model, enabling…
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I will introduce In-Context Operator Networks (ICON), a framework in which a single neural network learns solution operators for differential equations directly from a few prompted input-output examples at inference time, without any weight updates. ICON acts as a few-shot learner across forward and inverse problems for…
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Open-world mobility describes dynamic real-world environments in which vehicles, robots, infrastructure, and people interact under partial observability, distribution shift, long-tail events, and changing operational conditions. These settings require AI systems that can jointly perform perception, semantic reasoning,…
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Graph neural networks (GNNs) have emerged as a powerful framework for learning from graph-structured data, yet their theoretical understanding—particularly regarding the behavior of different architectural choices across various graph-based tasks—remains limited. In parallel, random geometric graphs (RGGs) provide a…
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Deep learning models, despite their power, lag behind the biological brain in interpretability, energy efficiency, and physical plausibility. This presentation explores the mathematical design principles of biological neural circuits -- building upon the classic concept that neural activity is fundamentally driven by…
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As the Deputy Director for Innovation and Emerging Technologies at the California Governor’s Office of Business and Economic Development (GO-Biz), Tre Bradley is the primary advisor to California state leaders on technology trends and opportunities, focusing on commercialization, technology transfer, and talent pipeline…
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We seek the ability to take a few images of a scene of interest, and turn it into an immersive visual experience, where one can explore it from different viewpoints, in effect visualizing a 3D representation of an object, scene or photograph, and providing numerous applications in augmented reality, e-commerce and 3D…
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The experimental sciences have long relied on labor-intensive, manual workflows that limit scalability and slow the pace of research and discovery. Despite advances in instrumentation and data analysis, the act of performing physical experiments remains time-consuming and resource- intensive, ultimately limiting the…