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    • Penn AI Seminar Series: Geometric and Physics Stucture Preservation in Scientific Machine Learning

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

    Geometric and Physics Stucture Preservation in Scientific Machine Learning Nat Trask, Associate Professor in Mechanical Engineering and Applied Mechanics at Penn Engineering, will share about his work constructing real-time digital twins built upon a data-driven finite element exterior calculus; constructing auto-regressive integrators with guaranteed long-term stability independent of rollout length; and constructing data-driven particle models […]

    ASSET Seminar: “Provable vs Impossible Trust: Reasoning, Steering, and Safety”

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

    Abstract: In this talk, I will discuss a collection of highlights from our recent work in trustworthy AI. (1) Certifying reasoning explanations with reliability guarantees and aligning with expert knowledge, (2) Simple yet effective steering inspired from theoretical rule-following mechanisms for transformers, and (3) The impossibility of monitoring stateless attackers and what safety defenses should […]

    CBE Seminar: “Water–Hydrophobe Interfaces: From Debunking Myths to Boosting Global Food–Water–Climate Resilience” (Himanshu Mishra, KAUST)

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    Wu & Chen Auditorium

    Abstract: First, I will outline our work on overcoming barriers to desert rehabilitation for urban greening, landscaping, and regional food security. In arid regions, freshwater is scarce, sandy soils lose water and fertilizers rapidly, which stifles plant growth. Our team has developed two complementary solutions: (i) Superhydrophobic Sand (SHS)1 — a plastic-free, bio-inspired mulch that […]

    FOLDS Seminar: Positive random walks and positive-semidefinite random matrices

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

    On the real line, a random walk that can only move in the positive direction is very unlikely to remain close to its starting point. After a fixed number of steps, the left tail has a Gaussian profile, under minimal assumptions. Remarkably, the same phenomenon occurs when we consider a positive random walk on the […]

    BE Doctoral Dissertation Defense: Aoife O’Farrell

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    The Department of Bioengineering at the University of Pennsylvania and Dr. Arjun Raj are pleased to announce the Doctoral Dissertation Defense of Aoife O'Farrell. Title: Stimulus Specificity of Trained Immune Memory in Human Macrophages Advisor: Dr. Arjun Raj Date and Time: Thursday, September 4th at 2:00 PM Location: Smilow 12-146AB Zoom Link: https://upenn.zoom.us/j/94438324577 The public is welcome to attend.

    BE Doctoral Dissertation Defense: Michael Yao

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    The Department of Bioengineering at the University of Pennsylvania and Drs. Osbert Bastani and James Gee are pleased to announce the Doctoral Dissertation Defense of Michael Yao. Title: Distributionally Robust Machine Intelligence Advisors: Osbert Bastani, PhD and James Gee, PhD Date & Time: Friday, September 5th at 11am Location: Amy Gutmann Hall, Room 414 (3317 Chestnut St, Philadelphia, PA 19104) RSVP […]

    Future Proof Your Research With Rigor

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    Bodek Lounge, Houston Hall 3417 Spruce St, Philadelphia, PA, United States

    What’s the best way to avoid paper retractions and irreproducible results? Conduct rigorous research. Join us for a public talk with Ivan Oransky, Co-Founder of Retraction Watch, Anita Anita Bandrowski, founder of The Research Resource Identification Initiative (RRID), and Jason Williams, Assisant Director of the DNA Learning Center, Cold Spring Harbor Laboratory, on doing science […]

    MEAM Seminar: MEAM Faculty Showcase

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

    Please join us on Tuesday, September 9 for an overview of research being done in the MEAM Department, hosted by MEAM Department Chair, Dr. Kevin Turner. This is an excellent opportunity for current graduate students to learn about the breadth of work being done in MEAM. The following faculty will be presenting (not in order […]

    ASSET Seminar: “Rethinking Test-Time Thinking: From Token-Level Rewards to Robust Generative Agents”

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

    We present a unified perspective on test-time thinking as a lens for improving generative AI agents through finer-grained reward modeling, data-centric reasoning, and robust alignment. Beginning with GenARM, we introduce an inductive bias for denser, token-level reward modeling that guides generation during decoding, enabling token-level alignment without retraining. While GenARM targets reward design, ThinkLite-VL focuses on the data […]

    Fall 2025 GRASP SFI: Tairan He, Carnegie Mellon University & NVIDIA, “Scalable Sim-to-Real Learning for General-Purpose Humanoid Skills”

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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 Humanoids represent the most versatile robotic platform, capable of walking, manipulating, and collaborating with people in human-centered environments. Yet, despite recent advances, building humanoids that can operate reliably in the real world remains a fundamental challenge. Progress has […]

    CBE Seminar: “Non-equilibrium Dynamics of Lipid Vesicles using Automated Flow Control” (Charles Schroeder, Princeton University)

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    Wu & Chen Auditorium

    Abstract: Vesicles are membrane-bound compartments that play a central role in biology. Despite recent progress, the dynamics of single- and multi-component lipid vesicles are not fully understood, particularly far from equilibrium where complex nonspherical shapes undergo large deformations in flow. In this talk, I will present recent work from our group on the non-equilibrium dynamics […]

    FOLDS seminar: Algorithmic stability for regression and classification

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

    In a supervised learning setting, a model fitting algorithm is unstable if small perturbations to the input (the training data) can often lead to large perturbations in the output (say, predictions returned by the fitted model). Algorithmic stability is a desirable property with many important implications such as generalization and robustness, but testing the stability […]