We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along […]
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. This seminar will NOT be recorded. ABSTRACT Recent years have witnessed remarkable […]
Abstract: Proteins perform many important biological functions while also avoiding fouling in an aqueous and crowded cellular environment. Their surfaces have evolved to be both chemically heterogeneous (containing nonpolar, polar, […]
A panel hosted by Penn Engineering Entrepreneurship (EENT) highlighting the importance of AI for the next generation of leaders and showcasing how Penn has been, and will continue to be, at the […]
Polymers are ubiquitous in both synthetic and biological materials and underlie technologies as diverse as surfactants, adhesives, proteins and DNA. One of the defining features of all polymeric materials is […]
Click here for more details. The workshop will explore the state of the art in sustainable computing and share recent research at the intersection of technology, economics, and policy. Through invited talks, […]
This will be an IN-PERSON event ONLY with in-person attendance in AGH 306. ABSTRACT Robots show great promise if they can get out of the lab into the field and […]
Critical to advancing immunotherapy and cell therapy in cancer is developing a deeper understanding pf the dynamics of immune cell-mediated cytotoxicity. The results from the multidisciplinary effort reported here include […]
Please RSVP here. We are living in an age where capabilities previously thought to be hallmarks of human intelligence are increasingly being replicated, or at least mimicked, in artificial systems. […]
Penn Engineering alumni working in AI will share industry insights in a panel discussion, followed by student networking sessions. Schedule: 2–3 p.m. Panel Discussion and Q&A 3:15–4 p.m. Breakout Networking Sessions […]
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 […]
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 […]