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    • MSE Seminar: “Hybrid Quantum-Classical Computing – Useful Quantum Devices in the NISQ Era” – Burns Healy – Dell Technologies

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    Wu and Chen Auditorium (Room 101), Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

    After an introduction to quantum computing, we discuss the currently practiced framework of "hybrid" computing, which is a method of accelerating traditional High Performance Compute (HPC) with Quantum Processing Units. I will talk about some of the ways in which Dell's research office has implemented this idea and the value it can bring to modern […]

    ESE Fall Seminar – “From Circuits to Cognition: Silicon for Embodied Intelligence”

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    Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

    The next generation of intelligent and autonomous systems requires not only novel devices but also new silicon architectures and design workflows that transcend conventional approaches to deliver real-time learning, perception, and decision-making under severe power and resource constraints. In this talk, I will outline a cross-layer methodology for architecting silicon for embodied AI, from workload […]

    FOLDS seminar: Weak to Strong Generalization in Random Feature Models

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    Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

    Zoom link: https://upenn.zoom.us/j/98220304722   Weak-to-Strong Generalization (Burns et al., 2023) is the phenomenon whereby a strong student, say GPT-4, learns a task from a weak teacher, say GPT-2, and ends up significantly outperforming the teacher. We show that this phenomenon does not require a strong and complex learner like GPT-4, nor pre-training. We consider students and […]

    CIS Seminar: “Inverse Problems using Generative Priors”

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    Wu and Chen Auditorium (Room 101), Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

    Inverse problems seek to recover an unknown source signal X for which we have indirect, partial, or noisy measurements Y. Most real-world inverse problems are ill-posed and the conventional line of attack has been to assume some structure (or prior) on X. Unfortunately, priors are not always available and often challenging to model mathematically. Generative […]

    Fall 2025 GRASP on Robotics: Kris Hauser, University of Illinois at Urbana-Champaign & Samsung Research America, “Modeling and Reasoning About ‘Stuff’”

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    Wu and Chen Auditorium (Room 101), Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

    This event will be in-person ONLY in Wu and Chen Auditorium. ABSTRACT Prevailing models in robotics reason about the world either as images (end-to-end learning approaches) or as a collection of rigid objects (classical approaches), but neither have proven to be suitable abstractions for manipulating cloth, ropes, piles of objects, plants, and natural terrain. My […]

    • Fall 2025 GRASP Seminar: Aljoša Ošep, NVIDIA, “Segmenting More Than Meets the Eye: Towards Amodal 4D Segmentation”

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    Amy Gutmann Hall, Room 306 3317 Chestnut Street, Philadelphia, PA, United States

    This is an in-person event ONLY in AGH 306. ABSTRACT The future of AI is embodied — imagine intelligent agents that can navigate and manipulate the world, from robot assistants helping around the home to autonomous vehicles taking you anywhere safely. To act in the physical world, these agents must do more than process raw […]

    Tedori-Callinan Distinguished Lecture: “Robotic Predictions are Hard, Especially About the Future”

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    Wu and Chen Auditorium (Room 101), Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

    Many autonomous systems (e.g, driverless cars and drones) must make decisions based on predictions of the future actions of other nearby agents, whose dynamics and intentions are unknown. E.g., autonomous cars must predict the motions of surrounding vehicles, pedestrians and bicycles. Autonomous racing drones must avoid crashing into other drones on the race course. Unfortunately, […]

    ESE Fall Seminar – “Diamond and GaN: Wide-Bandgap Allies for Thermal and Power Management from Devices to 3D-Stacked Chips”

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    Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

    Once considered exotic, diamond and gallium nitride (GaN) have become practical enablers for next-generation electronic systems. Their convergence—diamond providing exceptional thermal conductivity and GaN delivering high-efficiency power conversion—lays the groundwork for integrated thermal–power co-design. As computing, RF, and high-performance systems push toward higher power densities, conventional packaging and cooling approaches struggle to manage buried hotspots […]

    Penn AI Seminar Featuring Li Shen: Harnessing Trustworthy AI and Informatics for Dementia and Aging Research

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    Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

    Alzheimer’s disease and related dementias (ADRD) remains a major health crisis with profound social and economic burdens. Innovative strategies are needed to identify genetic risk and protective factors, model disease mechanisms, and accelerate therapeutic discovery. Advances in trustworthy AI and informatics now enable the integration of multimodal genetics, omics, imaging, and outcome data from large […]

    CIS Seminar: “Good Old Fashioned Engineering Can Close the 100,000 Year “Data Gap” in Robotics”

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    Wu & Chen Auditorium

    AI is rapidly advancing the way we think, but we live in a material world. We still need to move things, make things, and maintain things. We need AI-driven robots to support an aging human population that doesn’t have enough workers.  Large vision-language models based on internet-scale data can now pass the Turing Test for […]

    ASSET Seminar: “The coverage principle in language models: From pre-training to test-time scaling”

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    Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

    Test-time compute has emerged as a new axis for scaling language model capabilities, yet we lack a principled understanding of this paradigm. What are the right algorithms and trade-offs for test-time scaling? What properties of the pre-trained model enable it? And can we better align pre-training recipes for test-time success? This talk addresses these questions […]