• ESE Ph.D. Seminar: “Temporal Knockoffs: Variable selection for time-varying systems with e-processes”

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

    One of the primary goals of ‘explainable AI’ is the identification of a small subset of explanatory variables in an attempt to understand interesting phenomena. The Markov blanket constitutes one such subset, essential for tasks involving causal interpretation, prediction, and robustness. In medical imaging, identifying such variables is particularly important for achieving generalization across sites […]

    FOLDS SEMINAR: The Hidden Width of Deep ResNets

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

    Zoom link: https://upenn.zoom.us/j/6130182858   We present a mathematical framework to analyze the training dynamics of deep ResNets that rigorously captures practical architectures (including Transformers) trained from standard random initializations. Our approach combines stochastic approximation of ODEs with propagation-of-chaos arguments to obtain tight convergence rates to the “infinite size” limit of the dynamics. It yields the […]

    MEAM Seminar: “Bioelastic State Recovery for Haptic Sensory Substitution”

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

    The rich set of mechanoreceptors found in human skin offers a versatile engineering interface for transmitting information and eliciting perceptions, potentially serving a broad range of applications in patient care and other important industries. Targeted multisensory engagement of these afferent units, however, faces persistent challenges, especially for wearable, programmable systems that need to operate adaptively […]

    ESE Fall Seminar – “Engineering with Atomic-Scale Building Blocks: From Complex Properties to Functional Devices”

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

    As the demand for computing power and complexity continues to grow, developing new paradigms of information processing is essential. Unconventional functionalities arising from atomically engineered materials offer pathways to address these challenges. This has motivated the rapid development of atomic-scale materials as building blocks for future nanosystems. Their integration into functional devices, however, is hindered […]

    Fall 2025 GRASP SFI: Martin Nisser, University of Washington, “Computational Fabrication and Assembly for In Situ Manufacturing”

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

    This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.  ABSTRACT The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster […]

    CBE Seminar: “Engineering Soft Matter Systems through the Lens of Plant Physiology” (Jean-François Louf, Auburn University)

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    Wu & Chen Auditorium

    Abstract: Plants sense local pressure changes in their vasculature and transmit them across tissues via poroelastic coupling, triggering ionic currents in distant mechanosensitive cells to guide growth and biochemical responses. Inspired by this natural mechanotransduction, my lab develops synthetic analogs across soft materials. I will first present a soft robotic skin that mimics plant vasculature, […]

    MSE Seminar : “Semiconducting Materials for Opto/Bioelectronic Applications; Chemistry, Processing and Device Engineering” Antonio Facchetti – Georgia Institute of Technology

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

    In this presentation we report the realization of novel semiconductor materials, as well as thin-film processing and morphology engineering, for flexible and stretchable organic electronic devices such as thin film transistors, solar cells, electrolyte gated transistors, sensors and neuromorphic circuits. On material development, we present “soft” small-molecules and polymers by co-polymerizing pi-deconjugated building blocks, properly […]

    FOLDS seminar: Learning in Strategic Queuing

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

    Zoom link: https://upenn.zoom.us/j/98220304722   Over the last two decades we have developed good understanding how to quantify the impact of strategic user behavior on outcomes in many games (including traffic routing and online auctions) and showed that the resulting bounds extend to repeated games assuming players use a form of learning (no-regret learning) to adapt to […]

    ESE Ph.D. Thesis Defense: “Microfabricated devices for in-vivo sensing for mitochondrial assessment”

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    Room 221, Singh Center for Nanotechnology 3205 Walnut Street, Philadelphia, PA, United States

    Monitoring of oxygen concentration in biological tissues is essential for understanding cellular metabolism, mitochondrial function, and tissue regeneration. Mitochondrial dysfunction is linked to a wide range of metabolic and degenerative diseases, yet current diagnostic approaches lack the capability to continuously measure oxygen and metabolite dynamics in vivo. This work introduces an implantable electrochemical sensor platform […]

    Fall 2025 GRASP on Robotics: Jan Peters, Technische Universität Darmstadt & German Research Center for Artificial Intelligence, “Inductive Biases for Robot Learning”

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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 The quest for intelligent robots capable of learning complex behaviors from limited data hinges critically on the design and integration of inductive biases—structured assumptions that guide learning and generalization. In this talk, Jan Peters explores the foundational role of inductive biases in robot […]