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    • PICS Colloquium: Complex Polymer Design in the Age of AI: Why, What, and How?

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    PICS Conference Room 534 - A Wing , 5th Floor 3401 Walnut Street, Philadelphia, PA, United States

    Polymers are essential to a wide range of technologies, yet designing them with targeted structural and functional properties remains a grand challenge. A major opportunity lies in applying machine learning to help navigate the vast combinatorial design space—spanning sequence, composition, architecture, morphology, processing, and more—to discover new formulations or replace existing ones with more sustainable […]

    MSE Undergraduate Open House

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    LRSM Reading Room 3231 Walnut St., Philadelphia, PA, United States

    Materials Science and Engineering Undergraduate Open House Are you a first-year Student? Undecided about your major? Curious about MSE? Join us for food and fun and explore how MSE can transform your future! • Who Should Attend: All first-year undergrad engineering students, regardless of major • What: Eat good food and meet MSE faculty, staff, […]

    MEAM Seminar: “Controlling Friction and Wear via Engineered Surfaces and Advanced Nanomaterials”

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

    Friction and wear of moving components across various industries result in reliability issues, enormous energy losses, and environmental problems. These problems originate from the complex interactions between micro- and nanoscale asperities at the contacting surfaces. Such tribological challenges can be addressed via surface engineering, inspired by biological species that control friction very efficiently, combined with […]

    Democratic Repercussions of Media Fragmentation

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    Does media fragmentation contribute to democratic erosion? If so, how, and what steps are required to address potential impacts? Join the Penn Center on Media, Technology, and Democracy as we explore this topic both through the lens of empirical research - as represented by Professors Duncan Watts, Sandra González-Bailón, and Rasmus Kleis Nielsen along with […]

    AI Industry Days

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    The Penn Engineering community is invited to join our inaugural AI Industry Days on Wednesday, October 22 and Thursday, October 23. Continuing Industry Days’ tradition of providing avenues for students to learn, connect, and explore career opportunities in niche areas of engineering and technology, this series offers both in-person and virtual programming.   A variety […]

    ASSET Seminar: “Unpacking the Unintended Consequences of AI in Education”

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

    The rapid integration of AI into educational settings presents opportunities and challenges—this talk will discuss findings from three large-scale field studies investigating the impact of AI on student learning. First, we found that unfettered access to ChatGPT negatively impacted short-term student learning outcomes. Second, to understand longer-term effects, we examined learning in chess academies. Contrary […]

    ESE Guest Seminar: “The Versatility of Perovskite Materials for Optoelectronics”

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    Greenberg Lounge (Room 114), Skirkanich Hall 210 South 33rd Street, Philadelphia, PA, United States

    Perovskite solar cells (PSCs) have created much excitement in the past years and attract spotlight attention. This talk will provide an overview of the reasons for this development highlighting the historic development as well as the specific material properties that make perovskites so attractive for the research community. The current challenges are exemplified using a […]

    Fall 2025 GRASP SFI: Suraj Nair, Physical Intelligence, “Scaling Robot Learning with Vision-Language-Action Models”

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

    This speaker will present virtually. This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.  ABSTRACT The last several years have witnessed tremendous progress in the capabilities of AI systems, driven largely by foundation models that scale expressive architectures with diverse data sources. While the impact of this technology […]

    CBE Seminar: “Dynamic Interactions Between Copper Active Sites in Zeolites During NOx Pollution Abatement Catalysis” (Rajamani Gounder, Purdue University)

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    Wu & Chen Auditorium

    Abstract: The selective catalytic reduction (SCR) of nitrogen oxides (NOx) with ammonia using Cu-exchanged zeolites is a pollution abatement technology used commercially in diesel emissions control. At low temperatures (<523 K), Cu ion active sites become solvated by ammonia reactants to form homogeneous-like copper coordination complexes that are bonded ionically to anionic aluminum centers in […]

    MSE Seminar: “On-Chip Topological Photonics for terahertz 6G to XG Wireless” – Ranjan Singh – University of Notre Dame

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    Wu & Chen Auditorium

    The era of global digitalization and the increasing prevalence of artificial intelligence-driven data applications have set their sights on terabits per second (Tbps) communication links. The limitations of the rapidly advancing 5G network in meeting this demand, attributed to challenges such as bandwidth scarcity, have spurred the exploration of innovative technologies for the envisioned 6G […]

    ESE Fall Seminar – “New Pathways for Energy Efficient Computing Hardware”

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

    This winter the Americans will see a price hike in their electricity bill – not because of any issues related to energy generation but rather because of the rapidly increasing energy demand by the Data Centers.   Energy efficiency is becoming critical not only to maintain the incessant advanced march of computing, but also to ensure […]

    FOLDS seminar: An Information Geometric Understanding of Deep Learning

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

    Zoom link: https://upenn.zoom.us/j/98220304722   I will argue that properties of natural data are what predominantly make deep networks so effective. To that end, I will show that deep networks work well because of a characteristic structure in the space of learnable tasks. The input correlation matrix for typical tasks has a “sloppy” eigenspectrum where eigenvalues decay […]