August 20, 2026
While the movement of fluid through rigid systems is a well-characterized classical problem, predicting the behavior of fluid flow in compliant systems, in which structural deformation and the behavior of […]
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MEAM Ph.D. Thesis Defense: “Neural Operator Design From A Test-Time Learning Perspective”
August 21, 2026
Neural operator models offer a path toward modeling physical systems at low computational cost by learning mappings between function spaces. However, as mesh sizes and model parameter counts scale up, […]
Read MoreASSET Seminar: “Toward a Science of Auditing AI Systems: The Case of Election-Related Information”
August 26, 2026
As AI systems increasingly mediate access to information, we need better ways to understand how they behave in practice. In this talk, I use election-related information as my main case […]
Read MoreMSE Society: Ice Cream Social – Liquid Nitrogen
September 2, 2026
On Wednesday, September 2, the Penn Materials Science and Engineering MSE Society will host its annual Liquid Nitrogen Ice Cream Social from 5:00–6:00 p.m. in the Quain Courtyard. Join us […]
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Events for September 19, 2024
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MEAM Ph.D. Thesis Defense: “Contacts with Dynamically Tunable Adhesion and Friction via Active Materials with Thermally Modulated Stiffness”
Contact interactions, including adhesion and friction, are critical to the design of many engineered systems. Currently, most systems rely on materials with static mechanical properties, requiring careful selection of materials […]
MSE Seminar: “Materials, Mechanics, and Performance of Flexible Polymeric Actuators in Robotics” (Sameh Tawfick – University of Illinois, Urbana-Champaign)
My group is developing a roadmap for soft actuating materials to replace bulky electric motors in miniature robots requiring large mechanical work output. First, I will describe the materials microstructure and […]
CIS Seminar: “Learning Controllers for multi-robot Teams”
We have recently demonstrated the possibility of learning controllers that are zero-shot transferable to groups of real quadrotors via large-scale, multi-agent, end-to-end reinforcement learning. We train policies parameterized by neural […]