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    • ESE Ph.D. Thesis Defense: ”Manifold Filters and Neural Networks: Geometric Graph Signal Processing in the Limit”

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    Amy Gutmann Hall, Room 515 3317 Chestnut Street, Philadelphia, United States

    Graph Neural Networks (GNNs) are the tool of choice for scalable and stable learning in graph-structured data applications involving geometric information. My research addresses the fundamental questions of how GNNs can generalize across different graph scales and how they can remain stable on large-scale graphs. I do so by considering manifolds as graph limit models. […]

    ASSET Seminar: “Learning Reliable and Robust Generative Intelligence”

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

    Abstract: Robust simulation and precise modeling of physical dynamics are essential for advancing perception, planning, and control in the development of generalist physical agents. In this talk, I will present my research on building generative models that combine physical realism with scalability in high-dimensional environments. The presentation delves into both the theoretical foundations and practical […]

    ESE Ph.D. Thesis Defense: “Training Adaptive and Sample-Efficient Autonomous Agents”

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    Room 512, Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

    AI agents, both in the physical and digital worlds, should generalize from their training data to three increasingly difficult levels of deployment: training tasks and environments, training tasks and environments with variations, and completely new tasks and environments. Moreover, like humans, they are expected to learn from as little training data as possible, especially in […]

    Spring 2025 GRASP SFI: Anastasia Bizyaeva, Cornell University, “Nonlinear dynamics of social decision-making and belief formation”

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    Levine 307 3330 Walnut Street, Philadelphia, PA, United States

    This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. ABSTRACT Motivated by the study of complex social behavior and by the bottom-up design of collaborative autonomy, we present and analyze a nonlinear dynamic model of social belief formation. In our framework, belief updates of individuals are informed by the interplay […]

    CBE Seminar: “Computational Design and Simulations of Soft Matter: From Molecular Insights to Functional Materials” (Antonia Statt, UIUC)

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    Wu & Chen Auditorium

    Abstract: I will present the phase separation behavior of different sequences of a coarse-grained model for sequence defined macromolecules. They exhibit a surprisingly rich phase behavior, and not only conventional liquid-liquid phase separation is observed, but also reentrant phase behavior. Most sequences form open phases consisting of large, interconnected aggregates (e.g. string-like or membrane-like clusters), […]

    MSE Seminar: “Foundry Enabled Chip-Scale Photonics Technology and Applications” Shaya Fainman – University of California, San Diego (UCSD)

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

    Dense photonic integration requires miniaturization of materials, devices, circuits and systems, including passive components (e.g., engineered composite metamaterials, filters, etc.), active components (e.g., modulators and nonlinear wave mixers) and integrated circuits (Fourier transform spectrometer, programmable phase modulator of free space modes, linear algebra processors, etc.). In this talk we will discuss recent progress in developing […]

    IDEAS/STAT Optimization Seminar: Resilient Distributed Optimization for Cyberphysical Systems

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

    Zoom link: https://upenn.zoom.us/j/98220304722   Abstract: This talk considers the problem of resilient distributed multi-agent optimization for cyberphysical systems in the presence of malicious or non-cooperative agents. It is assumed that stochastic values of trust between agents are available which allows agents to learn their trustworthy neighbors simultaneously with performing updates to minimize their own local […]

    CIS Seminar: “Correctness Matters: Automatic Software Engineering in the age of Generative AI”

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    Levine 307 3330 Walnut Street, Philadelphia, PA, United States

    Software engineers never start from a blank page, but rather from an extant and usually long-running project in need of modification (for repair, extension, update, etc.). One way to view modern programming is thus as a continual process of iteratively transforming existing programs into something new, and hopefully better. In this talk, I will discuss […]

    ESE Ph.D. Thesis Defense: “Neural Compression: Estimating and Achieving the Fundamental Limits”

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    Amy Gutmann Hall, Room 515 3317 Chestnut Street, Philadelphia, United States

    Neural compression, which pertains to compression schemes that are learned from data using neural networks, has emerged as a powerful approach for compressing real-world data. Neural compressors often outperform classical schemes, especially in settings where reconstructions that are perceptually similar to the source are desired. Despite their empirical success, the fundamental principles governing how neural […]

    MSE PhD Defense: “Chromatin as an Active and Adaptive Material”

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    CEMB Conference room, LRSM 3231 Walnut Street, Room 112-C, Philadelphia, PA, United States

    The three-dimensional organization of chromatin within the cell nucleus plays a critical role in regulating gene expression, maintaining genome stability, and guiding cellular responses to environmental cues. Despite advances in imaging and sequencing technologies, the fundamental principles governing chromatin architecture and dynamics, particularly the role of associated proteins like HP1α in driving these processes, remain […]

    ESE Ph.D. Thesis Defense: “Machine Learning for Large-Scale Cyber-Physical Systems”

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

    Directly training deep learning models for applications in large-scale cyber-physical systems can be intractable due to the large number of components and decision variables. Instead, we focus on exploiting spatial symmetries in systems by designing size-generalizable architectures. Once trained on small-scale examples, such architectures exhibit equivalent or comparable performance on large-scale systems. The first example […]