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DTSTART;TZID=America/New_York:20251020T173000
DTEND;TZID=America/New_York:20251020T193000
DTSTAMP:20260403T143440
CREATED:20250922T193504Z
LAST-MODIFIED:20250922T193504Z
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SUMMARY:MSE Undergraduate Open House
DESCRIPTION:Materials Science and Engineering Undergraduate Open House \nAre you a first-year Student?\nUndecided about your major?\nCurious about MSE? \nJoin us for food and fun and explore how MSE can transform your future! \n• Who Should Attend: All first-year undergrad engineering students\, regardless of major \n• What: Eat good food and meet MSE faculty\, staff\, and undergraduate students \n• When: Monday\, October 20\, 2025 – 5:30 p.m. \n• Where: LRSM Reading Room – 3231 Walnut Street – 1st Floor \nQuestions?  Contact Vicky Lee\, Undergraduate Coordinator\, Department of Materials Science and Engineering – vickylt@seas.upenn.edu \nRSVP here by October 13:
URL:https://seasevents.nmsdev7.com/event/mse-undergraduate-open-house/
LOCATION:LRSM Reading Room\, 3231 Walnut St.\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251021T101500
DTEND;TZID=America/New_York:20251021T111500
DTSTAMP:20260403T143440
CREATED:20250916T193007Z
LAST-MODIFIED:20250916T193007Z
UID:10008510-1761041700-1761045300@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Controlling Friction and Wear via Engineered Surfaces and Advanced Nanomaterials"
DESCRIPTION: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 the incorporation of advanced nanomaterials at the sliding interfaces. Nanomaterials\, such as two-dimensional (2D) materials and nanoparticles\, have tremendous potential for such applications due to their unique physical and chemical properties and their ability to be incorporated as ultrathin protective surface coatings or nanoadditives in a liquid environment. \nI will discuss studies on 2D materials and nanoparticles for various tribological systems\, from demonstrating superlubric\, ultra-scratch-resistant transparent glass surfaces to achieving enhanced frictional anisotropy via bioinspired patterned surfaces combined with 2D materials. I will also discuss tribological behaviour of nanoparticle-based additives for next-generation liquid-based lubricant formulations\, where different types of nanoparticles as hybrid nanoadditives can significantly reduce friction and wear in lubricated sliding contacts.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-controlling-friction-and-wear-via-engineered-interfaces-and-advanced-nanomaterials/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251021T103000
DTEND;TZID=America/New_York:20251021T114500
DTSTAMP:20260403T143440
CREATED:20251008T144943Z
LAST-MODIFIED:20251008T144943Z
UID:10008534-1761042600-1761047100@seasevents.nmsdev7.com
SUMMARY:Democratic Repercussions of Media Fragmentation
DESCRIPTION: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 veteran journalist and media executive S. Mitra Kalita. \nRegister on Eventbrite or scan the QR code below \n \nThe Penn Center on Media\, Technology\, and Democracy works to better understand the information ecosystem through cutting-edge science\, and leverages that research to strengthen the foundations of democracy. Learn more about our Center and other upcoming events on our website.
URL:https://seasevents.nmsdev7.com/event/democratic-repercussions-of-media-fragmentation/
LOCATION:PA
ATTACH;FMTTYPE=image/jpeg:https://seasevents.nmsdev7.com/wp-content/uploads/2025/10/Media-Fragmentation-No-Logo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251022
DTEND;VALUE=DATE:20251024
DTSTAMP:20260403T143440
CREATED:20251003T160932Z
LAST-MODIFIED:20251003T160932Z
UID:10008527-1761091200-1761263999@seasevents.nmsdev7.com
SUMMARY:AI Industry Days
DESCRIPTION: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. \n  \nA variety of events are designed for Penn Engineering students\, including several Spotlight Sessions\, featuring Penn alumni and industry speakers; our Graduate Coffee Social\, for Master’s students\, PhD candidates\, and postdocs; and a Virtual Company Showcase on October 23. \nPenn Engineering faculty and staff who work with graduate students are invited to our Graduate Coffee Social\, taking place on October 22 from 1:45pm-3:15pm. Faculty and staff are asked to fill out a short form if they are interested in attending. \nA full list of confirmed events and details is available in Linktree\, including a Digital Program. AI Industry Days is organized by Penn Engineering Career Development and the Raj and Neera Singh Program in Artificial Intelligence.
URL:https://seasevents.nmsdev7.com/event/ai-industry-days/
LOCATION:PA
CATEGORIES:Student
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251022T120000
DTEND;TZID=America/New_York:20251022T131500
DTSTAMP:20260403T143440
CREATED:20250902T195040Z
LAST-MODIFIED:20250902T195040Z
UID:10008491-1761134400-1761138900@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Unpacking the Unintended Consequences of AI in Education"
DESCRIPTION: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 to the popular strategy of promoting student agency\, our findings show that self-regulated learning—where students decide when to request AI help—can substantially harm learning by diminishing engagement/motivation. Third\, we found that training students with “adversarial examples” significantly improved their ability to identify and correct ChatGPT-generated hallucinations\, enabling effective human-AI collaboration. Taken together\, these studies suggest that while providing students with unguided AI tools can be detrimental\, targeted interventions that train students to critically engage with AI can be beneficial. \n  \nZoom: https://upenn.zoom.us/j/95189835192
URL:https://seasevents.nmsdev7.com/event/asset-seminar-unpacking-the-unintended-consequences-of-ai-in-education/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="AI-enabled Systems%3A Safe%2C Explainable%2C and Trustworthy (ASSET) Center":MAILTO:asset-info@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251022T140000
DTEND;TZID=America/New_York:20251022T150000
DTSTAMP:20260403T143440
CREATED:20251008T140145Z
LAST-MODIFIED:20251008T140145Z
UID:10008533-1761141600-1761145200@seasevents.nmsdev7.com
SUMMARY:ESE Guest Seminar: "The Versatility of Perovskite Materials for Optoelectronics"
DESCRIPTION: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. \nThe current challenges are exemplified using a high-performance\, multicomponent system for PSCs (including Rb\, Cs\, methylammonium (MA)\, formamidinium (FA) perovskites). The resulting compositions exhibit higher performances\, resilience against external stressors and reproducibility. [1-4] \nUnfortunately\, many of the newly formulated liquid precursors often exhibit complex crystallization behaviour struggling to expel the typically used DMSO solvent. To delay the crystallization time\, two strategies are proposed to remove the strongly complexating DMSO molecules through a) modified processing of the liquid thin-film and b) a coordination solvent with a high donicity and a low vapor-pressure leading to a marked improvement in the overall film quality.[5] \nLastly\, interface manipulation\, especially on top of the formed perovskite\, is becoming a central topic to advance further. Typically\, this involves chemical surface treatments with a complex interaction. Here\, light annealing is introduced as a universal\, non-chemical approach to modify the perovskite surface resulting in a reduced surface recombination.[6] \n[1] McMeekin\, Saliba et al.\, Science (2016) \n[2] Saliba et al.\, Cesium-containing triple cation perovskite solar cells: improved stability\, reproducibility and high efficiency\, Energy & Environmental Science (2016) \n[3] M. Saliba et al.\, Incorporation of rubidium cations into perovskite solar cells improves photovoltaic \n[4] Turren-Cruz\, Hagfeldt\, Saliba\, Methylammonium-free\, high-performance and stable perovskite solar cells on a planar architecture\, Science (2018) \n[5] Zuo\,…\, Saliba; Coordination Chemistry as a Universal Strategy for a Controlled Perovskite Crystallization\, Advanced Materials (2023) \n[6] Kedia\,…\, Saliba; Light Makes Right: Laser Polishing for Surface Modification of Perovskite Solar Cells\, ACS Energy Letters (2023
URL:https://seasevents.nmsdev7.com/event/ese-guest-seminar-the-versatility-of-perovskite-materials-for-optoelectronics/
LOCATION:Greenberg Lounge (Room 114)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251022T150000
DTEND;TZID=America/New_York:20251022T160000
DTSTAMP:20260403T143440
CREATED:20251015T162006Z
LAST-MODIFIED:20251015T162006Z
UID:10008536-1761145200-1761148800@seasevents.nmsdev7.com
SUMMARY:Fall 2025 GRASP SFI: Suraj Nair\, Physical Intelligence\, “Scaling Robot Learning with Vision-Language-Action Models”
DESCRIPTION:This speaker will present virtually. This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.  \nABSTRACT\nThe 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 on vision and language understanding is abundantly clear\, its use in robotics remains in its infancy. Scaling robot learning still presents numerous open challenges—from selecting the right data to scale\, to developing algorithms that can effectively fit this data for closed-loop operation in the physical world. At Physical Intelligence\, we aim to tackle these questions. This talk will present our recent work on building vision-language-action models\, covering topics such as architecture design\, data scaling\, and open research directions.
URL:https://seasevents.nmsdev7.com/event/fall-2025-grasp-sfi-suraj-nair/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251022T153000
DTEND;TZID=America/New_York:20251022T163000
DTSTAMP:20260403T143440
CREATED:20250818T204039Z
LAST-MODIFIED:20250818T204039Z
UID:10008432-1761147000-1761150600@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Dynamic Interactions Between Copper Active Sites in Zeolites During NOx Pollution Abatement Catalysis" (Rajamani Gounder\, Purdue University)
DESCRIPTION:Abstract: \nThe 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 zeolite lattices. The ionic tethering of metal active sites to the zeolite host support confers localized mobility\, providing a mechanism for the dynamic and reversible interconversion of mononuclear and binuclear sites\, merging attractive features of both heterogeneous and homogeneous catalysts. We combine experimental and computational approaches to interrogate catalysts in operando under widely varying operating conditions\, including beyond those typical of commercial operation. We show that the effects of Cu ion mobility are preeminent for low-temperature NOx SCR reactivity and selectivity\, leading to dramatic differences in performance among Cu-zeolites of different bulk and atomic structure.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-dynamic-interactions-between-copper-active-sites-in-zeolites-during-nox-pollution-abatement-catalysis-rajamani-gounder-purdue-university/
LOCATION:Wu & Chen Auditorium
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251023T103000
DTEND;TZID=America/New_York:20251023T120000
DTSTAMP:20260403T143440
CREATED:20251006T200613Z
LAST-MODIFIED:20251006T200613Z
UID:10008530-1761215400-1761220800@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "On-Chip Topological Photonics for terahertz 6G to XG Wireless" - Ranjan Singh - University of Notre Dame
DESCRIPTION: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 and beyond (XG) communication. Terahertz (THz) micro-nanotechnologies\, leveraging semiconductor and quantum materials\, emerge as pivotal contenders for 6G\, promising ubiquitous connectivity and breaking down barriers between the physical\, digital\, and biological realms. \nDespite the potential\, current THz on-chip devices grapple with significant losses and restricted data speeds. In this context\, this talk will present topological insulator-inspired on-chip THz topological photonic integrated circuit for interconnects and wireless devices. These THz silicon devices feature low-loss\, broadband interconnects alongside wireless antennas and beamformers achieving speeds surpassing 300 Gbps. Silicon topological photonics is envisioned to pave the way for the advancement of CMOS-compatible hybrid electronic-photonic-driven terahertz technologies. These innovations are important for expediting the evolution of future 6G to XG communications\, facilitating real-time terabits per second connectivity. Such a capability extends to compute\, network sensing\, holographic communication\, cognitive internet\, and extensive digital replication of both the physical and biological realms.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-on-chip-topological-photonics-for-terahertz-6g-to-xg-wireless/
LOCATION:Wu & Chen Auditorium
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251023T110000
DTEND;TZID=America/New_York:20251023T120000
DTSTAMP:20260403T143440
CREATED:20250708T131045Z
LAST-MODIFIED:20250708T131045Z
UID:10008407-1761217200-1761220800@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "New Pathways for Energy Efficient Computing Hardware"
DESCRIPTION: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 that electronics does not become a drag on the finite energy resources of the world. This will need a radical rethinking of the basic building blocks that constitute the electronic hardware. In this talk\, I shall briefly present how exploiting physics and functional materials to augment CMOS may offer a new pathway for energy efficiency. In particular\, I shall discuss logic\, memory\, and backend technologies where we have achieved record performance by combining ultrathin ferroelectric materials with CMOS. These examples underscore how functional augmentation of CMOS by harnessing new materials could offer opportunities that are otherwise not available through conventional means.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tba/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251023T120000
DTEND;TZID=America/New_York:20251023T130000
DTSTAMP:20260403T143440
CREATED:20250828T202806Z
LAST-MODIFIED:20250828T202806Z
UID:10008476-1761220800-1761224400@seasevents.nmsdev7.com
SUMMARY:FOLDS seminar: An Information Geometric Understanding of Deep Learning
DESCRIPTION:Zoom link: https://upenn.zoom.us/j/98220304722 \n  \nI will argue that properties of natural data are what predominantly\nmake deep networks so effective. To that end\, I will show that deep\nnetworks work well because of a characteristic structure in the space\nof learnable tasks. The input correlation matrix for typical tasks has\na “sloppy” eigenspectrum where eigenvalues decay linearly on a\nlogarithmic scale. As a consequence\, the Hessian and the Fisher\nInformation Matrix of a trained network also have a sloppy\neigenspectrum. Using this idea\, I will demonstrate an analytical\,\nnon-vacuous PAC-Bayes bound on the generalization error for general\ndeep networks. \nI will show that the training process in deep learning explores a\nremarkably low dimensional manifold\, as low as three. Networks with a\nwide variety of architectures\, sizes\, optimization and regularization\nmethods lie on the same manifold. Networks being trained on different\ntasks (e.g.\, different subsets of ImageNet) using different methods\n(e.g.\, supervised\, transfer\, meta\, semi and self-supervised learning)\nalso lie on the same low-dimensional manifold. \nI will show that typical tasks are highly redundant functions of their\ninputs. Many perception tasks\, from visual recognition\, semantic\nsegmentation\, optical flow\, depth estimation\, to vocalization\ndiscrimination\, can be predicted extremely well regardless of whether\ndata is projected in the principal subspace where it varies the most\,\nsome intermediate subspace with moderate variability—or the bottom\nsubspace where data varies the least. \nReferences\n1. Does the data induce capacity control in deep learning? Rubing\nYang\, Jialin Mao\, and Pratik Chaudhari. [ICML ’22]\nhttps://urldefense.com/v3/__https://arxiv.org/abs/2110.14163__;!!IBzWLUs!Tq9FM96P-1mf3aRxklnZ7t8aLcjOIeWQz7icW_vh7HTMTDM2izgvjEC74IXkk0qZ7_TO9jbK-CF-J1f8wzalGOTVcA$\n2. The Training Process of Many Deep Networks Explores the Same\nLow-Dimensional Manifold. Jialin Mao\, Itay Griniasty\, Han Kheng Teoh\,\nRahul Ramesh\, Rubing Yang\, Mark K. Transtrum\, James P. Sethna\, Pratik\nChaudhari. [PNAS 2024]. https://urldefense.com/v3/__https://arxiv.org/abs/2305.01604__;!!IBzWLUs!Tq9FM96P-1mf3aRxklnZ7t8aLcjOIeWQz7icW_vh7HTMTDM2izgvjEC74IXkk0qZ7_TO9jbK-CF-J1f8wzYgaqrIWg$\n3. Many Perception Tasks are Highly Redundant Functions of their Input\nData. Rahul Ramesh\, Anthony Bisulco\, Ronald W. DiTullio\, Linran Wei\,\nVijay Balasubramanian\, Kostas Daniilidis\, Pratik Chaudhari.\n(in submission) https://urldefense.com/v3/__https://arxiv.org/abs/2407.13841__;!!IBzWLUs!Tq9FM96P-1mf3aRxklnZ7t8aLcjOIeWQz7icW_vh7HTMTDM2izgvjEC74IXkk0qZ7_TO9jbK-CF-J1f8wzaKl_77LQ$\n4. An Analytical Characterization of Sloppiness in Neural Networks:\nInsights from Linear Models. Jialin Mao\, Itay Griniasty\, Yan Sun\, Mark\nK Transtrum\, James P Sethna\, Pratik Chaudhari.\n(under review) https://urldefense.com/v3/__https://arxiv.org/abs/2505.08915__;!!IBzWLUs!Tq9FM96P-1mf3aRxklnZ7t8aLcjOIeWQz7icW_vh7HTMTDM2izgvjEC74IXkk0qZ7_TO9jbK-CF-J1f8wzYMqke2wg$
URL:https://seasevents.nmsdev7.com/event/folds-seminar-tba-5/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar,Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T100000
DTEND;TZID=America/New_York:20251024T120000
DTSTAMP:20260403T143440
CREATED:20251017T152016Z
LAST-MODIFIED:20251017T152016Z
UID:10008538-1761300000-1761307200@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: Katherine Mossburg
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. David Cormode are pleased to announce the Doctoral Dissertation Defense of Katherine Mossburg. \nTitle: “Developing Silver Sulfide-Based Nanoparticles for Imaging and Treatment of Breast Cancer”\nLocation: Alison Pouch (chair)\, Andrew Maidment\, David Issadore\nDate: Friday\, October 24\, 2025\nTime: 10:00 AM\nLocation: Raisler Lounge\, Towne 225 \nZoom: https://upenn.zoom.us/j/99943299118 \nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/mossburg/
LOCATION:PA
CATEGORIES:Dissertation or Thesis Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T103000
DTEND;TZID=America/New_York:20251024T114500
DTSTAMP:20260403T143440
CREATED:20250908T150154Z
LAST-MODIFIED:20250908T150154Z
UID:10008500-1761301800-1761306300@seasevents.nmsdev7.com
SUMMARY:Fall 2025 GRASP on Robotics: Alan Yuille\, Johns Hopkins University\, "3D Vision Language Models and Interactive World Models"
DESCRIPTION:This event will be in-person ONLY in Wu and Chen Auditorium. \nABSTRACT\nVision Language Models (VLMs) are extremely successful\, but their performance degrades when asked questions involving spatial relations and 3D world knowledge. Inspired by Cognitive Science\, we develop 3D VLMs which are 3D-aware and 3D-explicit to help us to diagnose their failure nodes. We present two approaches which involve developing datasets with 3D annotations for training the 3D VLMs.  The first works was developed on realistic-synthetic datasets and the 3D VLM is built on a 3D Image Parser. This 3D VLMs significantly outperform conventional VLMs for questions involving 3D/6D (Xingrui Wang et al. CVPR 2025 highlight) and physical reasoning (Xingrui Wang et al.\, ICLR 2025). This work is extended to complex images taking VLMs as base models and evaluated on a 3D comprehensive reasoning benchmark (W. Ma et al. ICCV 2026). We develop a 3D-VLM which significantly outperforms conventional VLMs  when asked questions requiring 3D knowledge (Wufei Ma et al. CVPR 2025 highlight). We further extend this approach to develop a 3D-VLM which performs even better and is also 3D-explicit (Wufei Ma et al. NeurIPS. 2025). We discuss the bigger picture which involves the need for world models as illustrated by (J. Chen et al. ICLR 2025)\, analysis by synthesis (T. Zheng et al. NeurIPS 2025)\, and early detection of cancer using radiology reports (P. Bassi et al. MICCAI 2025).
URL:https://seasevents.nmsdev7.com/event/fall-2025-grasp-on-robotics-alan-yuille-johns-hopkins-university-3d-vision-language-models/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T140000
DTEND;TZID=America/New_York:20251024T150000
DTSTAMP:20260403T143440
CREATED:20250829T153228Z
LAST-MODIFIED:20250829T153228Z
UID:10008483-1761314400-1761318000@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: Powering decarbonization with modeling and optimization of renewables in the multi-scale atmosphere with Michael F. Howland
DESCRIPTION:To meet net-zero carbon emissions targets by mid-century\, up to a ~30-fold increase in wind power capacity is required. Acceleration to this rate requires urgent improvements to efficiency and reliability of installed wind farms\, as well as cost reductions for future offshore farms. To expand energy production\, wind turbines are rapidly increasing in size\, wind farms are proliferating to new locations and are increasing in size and siting density\, and novel wind farm design and control methods are increasingly deployed. But current engineering models driving wind power design and control rely on idealized theory that neglects key aspects of the rotor aerodynamics and the turbulent atmospheric boundary layer\, which are increasingly important for larger turbines and farms. We revisit the first-principles of mass\, momentum\, and energy conservation to develop a Unified Momentum theory for rotors across operating regimes\, accounting for arbitrary misalignments between rotor and inflow and thrust coefficients. The model is validated against large eddy simulations and generalizes and replaces both classical momentum theory and the Betz limit. Going from the scale of a turbine to a farm\, wake losses can reduce farm energy by 30%\, a significant loss that negatively impacts economics and is increasing given wind power expansion. Using large eddy simulations of wind turbines operating in a range of atmospheric conditions\, we systematically uncover the significant roles of Coriolis effects and stability on wake recovery\, trajectory\, and morphology. A new fast-running wind farm model that accounts for the coupled rotor operational and atmospheric effects on wakes is developed. The wind farm model is leveraged for applications including collective control and for control co-design\, applied in both simulations and utility-scale field experiments. Collective control can increase the energy generation of wind farms through software modifications\, without additional turbines or hardware. Going from the scale of a wind farm to the energy system\, we leverage an integrated climate and energy system modeling framework to design minimum-cost decarbonized energy systems. Energy system optimization with high-resolution atmospheric predictions reveals opportunities for complementarity between spatiotemporal variations in wind and solar supply to align with energy demand and to lower the cost of decarbonized energy systems.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-powering-decarbonization-with-modeling-and-optimization-of-renewables-in-the-multi-scale-atmosphere-with-michael-f-howland/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251028T101500
DTEND;TZID=America/New_York:20251028T111500
DTSTAMP:20260403T143440
CREATED:20250919T134238Z
LAST-MODIFIED:20250919T134238Z
UID:10008515-1761646500-1761650100@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: “Manipulating Mechanical Wave Propagation with Phononic Materials”
DESCRIPTION:One grand challenge for materials and structures design is to satisfy multiple conflicting requirements. For example\, energy infrastructure\, especially those in remote and extreme environments such as offshore wind turbines and nuclear reactors\, requires components to operate effectively over long time periods and avoid catastrophic failures. Structural materials in aviation must be lightweight but high in strength\, stiff while dampening out harmful vibrations\, survive damaging impact events\, and interact with complex flows in non-detrimental ways. On smaller length scales\, acoustic and ultrasonic sensors require specific frequency and dissipative responses\, and need to detect wavelengths that are much smaller than their physical size. This talk focuses on a common theme to these critical engineering problems: understanding how mechanical waves interact with engineered materials across different length and time scales. In particular\, the field of phononic materials studies how engineering micro- and meso-scale features in materials and structures can prescribe the frequency and spatial properties of acoustic waves. Features such as spatial periodicity of the material or geometry\, resonant inclusions\, and nonlinearities can lead to wave propagation and modal properties not found in natural materials. New wave propagation phenomena have been discovered in these material platforms\, which has been a direct result of an interdisciplinary research approach\, integrating additive manufacturing\, acoustics\, mechanics\, materials science\, and design. This presentation will discuss our group’s recent research in phononic materials\, focusing on (1) effects of nonlinearity on wave propagation in phononic materials\, and (2) applications of phononic materials to passive flow control\, using reduced order models\, finite element simulations\, and experiments.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-manipulating-mechanical-wave-propagation-with-phononic-materials/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251028T153000
DTEND;TZID=America/New_York:20251028T163000
DTSTAMP:20260403T143440
CREATED:20251013T151815Z
LAST-MODIFIED:20251013T151815Z
UID:10008535-1761665400-1761669000@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Sparse View Synthesis"
DESCRIPTION:We seek the ability to take a few images of a scene of interest\, and turn it into an immersive visual experience\, where one can explore it from different viewpoints\, in effect visualizing a 3D representation of an object\, scene or photograph\, and providing numerous applications in augmented reality\, e-commerce and 3D photography.   This problem\, known as view synthesis or image-based rendering in computer vision and graphics\, has a three-decade plus history\, and is currently undergoing a renaissance with new representations of 3D geometry enabling unparalleled realism.  We discuss some of the history in terms of capturing the light field (the space of light rays for any spatial position and viewing direction)\, and our own work on a sampling theory for view synthesis based on light fields\, leading to the development of volumetric radiance fields as a fundamentally new approach to representing 3D geometry for view synthesis.  We will also discuss parallels to Monte Carlo and volumetric rendering and simulation problems in computer graphics.  We then ask the question of how far we can push the required number of images\, in order to achieve sparse view synthesis with very few images\, in the limit only one photograph.  In this context\, we also discuss our recent results on a number of applications including real-time live portraits\, generative AI for 3D scenes\, and differentiable light transport for inverse rendering.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-sparse-view-synthesis/
LOCATION:Wu & Chen Auditorium
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251029T120000
DTEND;TZID=America/New_York:20251029T131500
DTSTAMP:20260403T143440
CREATED:20250905T194514Z
LAST-MODIFIED:20250905T194514Z
UID:10008499-1761739200-1761743700@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: “​When Is a Conformal Set\, a Conformal Set?”
DESCRIPTION:The two most popular vehicles for communicating uncertainty in the estimates of an unknown quantity are confidence sets and conformal sets. The set produced and its corresponding probability guarantee (conditional on the feature vector) depend upon assumptions the analyst has made about the underlying data generating process. For example\, are the residuals independent and normally distributed? Is the data generating process exchangeable? Knowing only the underlying data upon which a model was built\, how should one assess the validity of a proffered confidence or conformal set? Second\, how should one use these sets to inform decision making? \n  \nZoom: https://upenn.zoom.us/j/95189835192
URL:https://seasevents.nmsdev7.com/event/asset-seminar-when-is-a-conformal-set-a-conformal-set/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="AI-enabled Systems%3A Safe%2C Explainable%2C and Trustworthy (ASSET) Center":MAILTO:asset-info@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251029T150000
DTEND;TZID=America/New_York:20251029T160000
DTSTAMP:20260403T143440
CREATED:20251015T164848Z
LAST-MODIFIED:20251015T164848Z
UID:10008537-1761750000-1761753600@seasevents.nmsdev7.com
SUMMARY:Fall 2025 GRASP SFI: Roberto Martín-Martín\, University of Texas at Austin\, “Making Mobile Manipulation Real: New Learning Paradigms for Robots”
DESCRIPTION:This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.  \nABSTRACT\nMost tasks people wish robots could do (fetching objects across rooms\, assisting in the kitchen\, tidying) require mobile manipulation\, the integration of navigation and manipulation. While robots have made remarkable progress in each skill independently\, bringing them together sequentially (navigate>manipulate>navigate...) or simultaneously (coordinating base and arm motion to open a fridge or wipe a table) remains one of the hardest challenges in robotics. The difficulty lies not only in mastering two complex capabilities\, but in coupling them safely and efficiently\, over long horizons\, under uncertainty\, and in contact‑rich settings. These conditions often break the assumptions of standard imitation and reinforcement learning\, which tend to struggle to generalize\, train safely\, and anticipate\, learn from\, and recover from errors in unstructured environments. I’ll present three learning algorithms from my lab designed specifically for mobile manipulation: methods that extract skills from in-the-wild human video (SafeMimic)\, learn structured action spaces that make RL sample-efficient on real robots (SLAC)\, and integrate memory mechanisms with foundation models to reason over extended tasks (Bumble). Our latest results demonstrate multi-step single-video imitation\, surface-wiping RL on wheeled mobile manipulators trained in real world under one hour\, and broad task generalization to novel objects in building-wide scale with improved trial efficiency. I’ll close with an analysis of failures and limitations and a roadmap for scaling: toward robots with the adaptability\, safety\, and fluency needed to make learning mobile manipulation an easy and reliable part of everyday life.
URL:https://seasevents.nmsdev7.com/event/fall-2025-grasp-sfi-roberto-martin-martin/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251029T153000
DTEND;TZID=America/New_York:20251029T163000
DTSTAMP:20260403T143440
CREATED:20250818T204522Z
LAST-MODIFIED:20250818T204522Z
UID:10008433-1761751800-1761755400@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Computational Approaches for Understanding and Engineering Biomolecular Condensates” (Jerelle Joseph\, Princeton University)
DESCRIPTION:Abstract: \nBiomolecular condensates are membraneless compartments inside living cells that play critical roles in health and disease. Over the past two decades\, a wide body of work has established that these compartments form through phase separation of molecules such as proteins and RNA. This discovery has sparked significant interest in uncovering the molecular factors that drive intracellular phase separation and in understanding how condensate material properties are encoded. In parallel\, condensates offer a versatile platform for engineering novel functions and preventing aberrant behaviors. To advance our ability to understand and engineer condensates\, our group develops computational models and molecular simulation approaches that enable us to zoom in on condensates and uncover how molecular interactions give rise to their complex behaviors\, while also supporting simulation-driven condensate design. Specifically\, by combining atomistic simulations with experimental data\, we design residue-resolution force fields that balance the speed and accuracy needed to model the dynamics of these multicomponent structures. In this talk\, I will discuss recent developments on this front.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-computational-approaches-for-understanding-and-engineering-biomolecular-condensates-jerelle-joseph-princeton-university/
LOCATION:Wu & Chen Auditorium
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251030T103000
DTEND;TZID=America/New_York:20251030T120000
DTSTAMP:20260403T143440
CREATED:20251017T193905Z
LAST-MODIFIED:20251017T193905Z
UID:10008539-1761820200-1761825600@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Hybrid Quantum-Classical Computing - Useful Quantum Devices in the NISQ Era" - Burns Healy - Dell Technologies
DESCRIPTION:After an introduction to quantum computing\, we discuss the currently practiced framework of “hybrid” computing\, which is a method of accelerating traditional High Performance Compute (HPC) with Quantum Processing Units. I will talk about some of the ways in which Dell’s research office has implemented this idea and the value it can bring to modern compute workloads\, including specific applications to materials science. Then I will do a deeper dive into a particular algorithm I wrote using HQCC to improve results for Dell’s supply chain team.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-hybrid-quantum-classical-computing-useful-quantum-devices-in-the-nisq-era-burns-healy-dell-technologies/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251030T110000
DTEND;TZID=America/New_York:20251030T120000
DTSTAMP:20260403T143440
CREATED:20250730T172652Z
LAST-MODIFIED:20250730T172652Z
UID:10008417-1761822000-1761825600@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "From Circuits to Cognition: Silicon for Embodied Intelligence"
DESCRIPTION:The next generation of intelligent and autonomous systems requires not only novel devices but also new silicon architectures and design workflows that transcend conventional approaches to deliver real-time learning\, perception\, and decision-making under severe power and resource constraints. In this talk\, I will outline a cross-layer methodology for architecting silicon for embodied AI\, from workload characterization and benchmarking to architecture exploration\, compiler integration\, and system prototyping. Central to this effort are compute-in-memory accelerators\, mixed-signal neuromorphic architectures\, and memory-centric SoCs that integrate hybrid RRAM/SRAM arrays\, all designed within workflow frameworks that couple algorithmic needs with hardware capabilities. Case studies will highlight reconfigurable streaming-dataflow architectures for reasoning and decision-making\, heterogeneous SoCs optimized for autonomous workloads\, and bio-mimetic silicon platforms for navigation and planning. By unifying design flows\, benchmarking\, and circuit innovation\, this work illustrates how silicon architectures can be systematically engineered to achieve the transparency\, energy efficiency\, and adaptability demanded by embodied intelligence and autonomy.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tba-6/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251030T120000
DTEND;TZID=America/New_York:20251030T130000
DTSTAMP:20260403T143440
CREATED:20250828T204046Z
LAST-MODIFIED:20250828T204046Z
UID:10008477-1761825600-1761829200@seasevents.nmsdev7.com
SUMMARY:FOLDS seminar: Weak to Strong Generalization in Random Feature Models
DESCRIPTION:Zoom link: https://upenn.zoom.us/j/98220304722 \n  \nWeak-to-Strong Generalization (Burns et al.\, 2023) is the phenomenon whereby a strong student\, say GPT-4\, learns a task from a weak teacher\, say GPT-2\, and ends up significantly outperforming the teacher. We show that this phenomenon does not require a strong and complex learner like GPT-4\, nor pre-training. We consider students and teachers that are random feature models\, described by two-layer networks with a random and fixed bottom layer and trained top layer. A ‘weak’ teacher\, with a small number of units (i.e. random features)\, is trained on the population\, and a ‘strong’ student\, with a much larger number of units (i.e. random features)\, is trained only on labels generated by the weak teacher. We demonstrate\, prove and understand\, how the student can outperform the teacher\, even though trained only on data labeled by the teacher\, with no pretraining or other knowledge or data advantage over the teacher. We explain how such weak-to-strong generalization is enabled by early stopping. Importantly\, we also show the quantitative limits of weak-to-strong generalization in this model. \nJoint work with Marko Medvedev\, Kaifeng Lyu\, Dingli Yu\, Sanjeev Arora and Zhiyuan Li.
URL:https://seasevents.nmsdev7.com/event/folds-seminar-tba-6/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar,Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251030T153000
DTEND;TZID=America/New_York:20251030T163000
DTSTAMP:20260403T143440
CREATED:20250826T132819Z
LAST-MODIFIED:20250826T132819Z
UID:10008459-1761838200-1761841800@seasevents.nmsdev7.com
SUMMARY:BE Seminar - Celeste M. Nelson "Mitochondria\, mechanics\, and tissue morphogenesis"
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/be-seminar-celeste-m-nelson-mitochondria-mechanics-and-tissue-morphogenesis/
LOCATION:216 Moore Building
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251030T153000
DTEND;TZID=America/New_York:20251030T163000
DTSTAMP:20260403T143441
CREATED:20251022T124521Z
LAST-MODIFIED:20251022T124521Z
UID:10008543-1761838200-1761841800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Inverse Problems using Generative Priors"
DESCRIPTION:Inverse problems seek to recover an unknown source signal X for which we have indirect\, partial\, or noisy measurements Y. Most real-world inverse problems are ill-posed and the conventional line of attack has been to assume some structure (or prior) on X. Unfortunately\, priors are not always available and often challenging to model mathematically. Generative models are powerful tools that learn patterns from data\, hence a new opportunity to obtain samples from the priors of X. Given such a prior sample\, it may be possible to compute how well the sample explains the measurement Y\, and iteratively guide the denoising process to generate samples from the posterior p(X|Y). This paradigm of posterior sampling is unlocking a wide range of applications that—before the deep learning era—were facing performance walls. This talk will introduce the core ideas in this paradigm\, generalize the framework\, and show how this framework can be applied to multiple applications\, including unsupervised speech separation\, zero-shot human pose tracking\, and inverse path planning.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-inverse-problems-using-generative-priors/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251031T103000
DTEND;TZID=America/New_York:20251031T114500
DTSTAMP:20260403T143441
CREATED:20250902T143732Z
LAST-MODIFIED:20250902T143732Z
UID:10008488-1761906600-1761911100@seasevents.nmsdev7.com
SUMMARY:Fall 2025 GRASP on Robotics: Kris Hauser\, University of Illinois at Urbana-Champaign & Samsung Research America\, “Modeling and Reasoning About 'Stuff'”
DESCRIPTION:This event will be in-person ONLY in Wu and Chen Auditorium. \nABSTRACT\nPrevailing models in robotics reason about the world either as images (end-to-end learning approaches) or as a collection of rigid objects (classical approaches)\, but neither have proven to be suitable abstractions for manipulating cloth\, ropes\, piles of objects\, plants\, and natural terrain. My lab is investigating novel representations of “stuff” that are built de novo from visual and tactile perception data\, whose properties are learned continuously through interaction. Volumetric Stiffness Fields\, Graph Neural Networks\, Neural Dynamics\, and 3D metric-semantic maps are examples of models that allow robots to learn about their environment without having preconceived notions of individual objects\, their physical properties\, or how they interact. For a variety of domains and materials\, these techniques are able to model complex interactions\, uncertainty\, and multi-modal correlations between appearance and physical properties. Applications will be shown in agriculture\, construction\, and household object manipulation. \n(This talk solely represents the research and opinions of Dr. Hauser under his UIUC affiliation\, and does not communicate any results\, statements\, or opinions on behalf of Samsung Research America\, Samsung Electronics\, or any of its affiliates.)
URL:https://seasevents.nmsdev7.com/event/fall-2025-grasp-on-robotics-kris-hauser-university-of-illinois-at-urbana-champaign-modeling-and-reasoning-about-stuff/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251103T130000
DTEND;TZID=America/New_York:20251103T140000
DTSTAMP:20260403T143441
CREATED:20251028T140215Z
LAST-MODIFIED:20251028T140215Z
UID:10008544-1762174800-1762178400@seasevents.nmsdev7.com
SUMMARY:Fall 2025 GRASP Seminar: Aljoša Ošep\, NVIDIA\, "Segmenting More Than Meets the Eye: Towards Amodal 4D Segmentation"
DESCRIPTION:This is an in-person event ONLY in AGH 306. \nABSTRACT\nThe future of AI is embodied — imagine intelligent agents that can navigate and manipulate the world\, from robot assistants helping around the home to autonomous vehicles taking you anywhere safely. To act in the physical world\, these agents must do more than process raw sensory inputs: they must reason about the underlying\, dynamic world that gives rise to their observations. This requires a 4D scene model — 3D geometry evolving over time — that is object-centric\, predictive\, and grounded in language. Such representations enable agents to answer questions like “Where am I?”\, “What is around me?”\, and “How can I interact with this object?” \nIn this talk\, I will advocate for an explicit\, amodal representation of world geometry and objects learned from unlabeled sequences. Such a model supports robust perception in dynamic environments and enables language-driven interaction with the world. I will outline a blueprint for building such systems\, centered around two complementary components: a slow video object mining method that discovers and tracks arbitrary objects in unlabeled videos\, and a fast feed-forward network that learns from these tracks to detect\, segment\, complete\, and forecast object trajectories. \nI will trace the progression from early methods for self-supervised object discovery and detection\, to recent models capable of promptable\, text-conditioned 4D segmentation and amodal scene completion. Taken together\, these components form a scalable recipe for learning object-centric 4D representations directly from raw video — a step toward grounded\, general-purpose world understanding.
URL:https://seasevents.nmsdev7.com/event/fall-2025-grasp-seminar-aljosa-osep-nvidia-segmenting-more-than-meets-the-eye-towards-amodal-4d-segmentation/
LOCATION:Amy Gutmann Hall\, Room 306\, 3317 Chestnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251104T101500
DTEND;TZID=America/New_York:20251104T111500
DTSTAMP:20260403T143441
CREATED:20250919T214213Z
LAST-MODIFIED:20250919T214213Z
UID:10008516-1762251300-1762254900@seasevents.nmsdev7.com
SUMMARY:Tedori-Callinan Distinguished Lecture: "Robotic Predictions are Hard\, Especially About the Future"
DESCRIPTION:Many autonomous systems (e.g\, driverless cars and drones) must make decisions based on predictions of the future actions of other nearby agents\, whose dynamics and intentions are unknown. E.g.\, autonomous cars must predict the motions of surrounding vehicles\, pedestrians and bicycles. Autonomous racing drones must avoid crashing into other drones on the race course. Unfortunately\, only partial and noisy data on the motions of these potential hazards are available. This talk will introduce a novel method to approximate\, in real-time\, a predictive Koopman operator for each potential hazard from noisy data\, quantify the uncertainty of the future predictions\, and use the quantified predictions to provide probabilistic collision avoidance guarantees within a real-time model predictive control framework. Experiments with ground robots\, a drone\, and a semi-autonomous crane on an ocean going vessel will illustrate the ideas.
URL:https://seasevents.nmsdev7.com/event/tedori-callinan-distinguished-lecture-robotic-predictions-are-hard-especially-about-the-future/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Distinguished Lecture
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251104T110000
DTEND;TZID=America/New_York:20251104T120000
DTSTAMP:20260403T143441
CREATED:20250730T152406Z
LAST-MODIFIED:20250730T152406Z
UID:10008416-1762254000-1762257600@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "Diamond and GaN: Wide-Bandgap Allies for Thermal and Power Management from Devices to 3D-Stacked Chips"
DESCRIPTION:Once considered exotic\, diamond and gallium nitride (GaN) have become practical enablers for next-generation electronic systems. Their convergence—diamond providing exceptional thermal conductivity and GaN delivering high-efficiency power conversion—lays the groundwork for integrated thermal–power co-design. As computing\, RF\, and high-performance systems push toward higher power densities\, conventional packaging and cooling approaches struggle to manage buried hotspots and multilayer bottlenecks. In this talk\, I’ll share our journey that began in 2015 with an unconventional idea—integrating thin-film polycrystalline diamond directly onto GaN high-electron-mobility transistors (HEMTs) while preserving their functionality. This effort led to some of our most significant findings\, including the development of a low-temperature (400–500 °C)\, back-end-of-line (BEOL)–compatible diamond growth platform\, now extended to silicon\, oxides\, and nitrides. Our “all-around” diamond-integrated GaN HEMTs achieved an average channel-temperature reduction of ~70 °C at 25 W/mm (DC) (IEDM ’22\, ’23)\, while workload-representative\, heater-based experiments demonstrated nearly a tenfold reduction in temperature rise within 3D architectures (IEDM ’24). In collaboration with Prof. Mitra’s team\, we are advancing the thermal scaffolding paradigm for 3D chips—a concept that merges materials innovation with architectural design. It is exciting to build upon nearly two decades of GaN and diamond research—dating back to my Ph.D. work on vertical GaN transistors—and to see it evolving toward compact\, energy-efficient\, and thermally optimized electronics for the AI datacenter era. Much of our research has been carried out in close collaboration with industry partners\, and some of our GaN efforts have already transitioned into industrial applications. I will also share some of the key lessons learned along the way\, as well as the challenges that continue to shape this evolving field.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tba-5/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium,Symposium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251104T120000
DTEND;TZID=America/New_York:20251104T140000
DTSTAMP:20260403T143441
CREATED:20251101T011500Z
LAST-MODIFIED:20251101T011500Z
UID:10008550-1762257600-1762264800@seasevents.nmsdev7.com
SUMMARY:Penn AI Seminar Featuring Li Shen: Harnessing Trustworthy AI and Informatics for Dementia and Aging Research
DESCRIPTION:Alzheimer’s disease and related dementias (ADRD) remains a major health crisis with profound social and economic burdens. Innovative strategies are needed to identify genetic risk and protective factors\, model disease mechanisms\, and accelerate therapeutic discovery. Advances in trustworthy AI and informatics now enable the integration of multimodal genetics\, omics\, imaging\, and outcome data from large biobanks\, creating powerful opportunities for biomarker and gene discovery beyond categorical diagnoses. At the same time\, generative AI and large language models (LLMs) extend these capabilities to text-rich sources such as the scientific literature\, clinical notes\, and caregiver narratives. When integrated with knowledge graphs\, LLMs can dynamically retrieve and synthesize domain-specific knowledge.
URL:https://seasevents.nmsdev7.com/event/penn-ai-seminar-featuring-li-shen-harnessing-trustworthy-ai-and-informatics-for-dementia-and-aging-research/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251104T153000
DTEND;TZID=America/New_York:20251104T163000
DTSTAMP:20260403T143441
CREATED:20251028T162239Z
LAST-MODIFIED:20251028T162239Z
UID:10008545-1762270200-1762273800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Good Old Fashioned Engineering Can Close the 100\,000 Year “Data Gap” in Robotics"
DESCRIPTION:AI is rapidly advancing the way we think\, but we live in a material world. We still need to move things\, make things\, and maintain things. We need AI-driven robots to support an aging human population that doesn’t have enough workers.  Large vision-language models based on internet-scale data can now pass the Turing Test for intelligence.  In this sense\, data has “solved” language and many claim that data has solved speech recognition and computer vision. \nWill data also solve robotics?  Rich Sutton points out in the “Bitter Lesson” that data and black-box “end-to-end” models have surpassed all the best-laid analytic work in AI.  I accept that this trend will eventually produce general-purpose robots.   \nBut the question is:  when? \nUsing commonly-accepted metrics for converting word and image tokens into time\, the amount of internet-scale data (texts and images) used to train contemporary large vision language models (VLMs) is on the order of 100\,000 years – it would take a human that long to read or view it [2].  However\, the data needed to train robots must combine video with robot motion commands:  that data does not yet exist.   \nOne way to collect robot data is teleoperation – where human “trainers” use remote control devices to painstakingly choose every motion of a robot as it performs a task – like folding a towel – over and over again.   This is a variant of puppeteering\, an ancient artform\, that requires extensive human skill and patience.  Unlike puppets however\, robot joint angles can be precisely recorded\, so the exact position history of each motor can be combined with videos from cameras that record the scene from different angles.  The data for each “trial” or “trajectory” from start to finish includes a few minutes of video and the position history of all robot motors.     Many companies are gearing up with fleets of robots and humans to collect data this way.   \nHowever\, the largest such dataset reported so far  is on the order of 1 year of data (it was collected in under a year by many human-robot systems).  This data has been used to train large models and initial results are intriguiging.  But this suggests that at current data-collection rates\, a general-purpose robot\, based on a ChatGPT-sized set of robot data\,  will be available in…100\,000 years. \nSo how  can we close this 100\,000-year “Data Gap”? \nResearchers are actively pursuing 2 additional methods for generating robot data: simulation and  3D analysis of internet videos. \nDigital simulation today looks incredibly life-like – consider the special effects in action movies and the deepfakes generated by AI.  It’s relatively easy to create life-like simulations of robot drones flying or robot dogs walking down stairs and doing backflips.  Simulations can also provide videos and motor data to train large robot models. Simulation data works well for robots that fly or walk\, or even for doing backflips.    But it turns out that simulation is notoriously unreliable for robot manipulation. \nThis Sim2Real “gap” arises because physical manipulation involves precise and changing contacts between the edges and surfaces of objects and grippers\, very small but important material deformations\, and very nuanced and changing frictional forces due to microscopic surface variations.  These factors are extremely difficult to measure and to accurately model.  But these very small errors result in simulation data that looks realistic but is physically inaccurate.  A submillimeter inaccuracy can make the difference between carrying a glass of water and dropping it.  Robots trained on simulation data can work well in simulation but they often fail when manipulating physical objects.  Researchers agree that physically-accurate simulation of manipulation is a Grand Challenge. \nThe third potential source of robot data is videos on the Internet.  YouTube includes about 35\,000 years of videos.  Many of these videos show people manipulating objects\, cooking\, stacking cups\, folding laundry.  However\, it is extremely difficult to extract precise 3D motion from 2D videos.  Computer vision researchers can approximately track the motion of human hands and objects in a video\, but the same issues of noise and precision make data from videos unreliable for robot learning.  Accurately “lifting” a video image back into 3D to recover precise finger and object motions is a Grand Challenge for computer vision that is not expected to be solved in the forseeable future. \nThere is a 4th option.   \nRobot data can be collected from real robots working with real objects in real environments. Industry has thousands of robots doing useful work around the clock.  Today\, little of this real robot production data is saved.  This is partly because most industrial robots perform extremely repetitive tasks like automotive welding and spot-painting that do not vary much.  Data to train large models is often diverse – think of the massive range of texts and images on the internet.  General-purpose robots need a broad range of data with variations in tasks\, objects\, and environments.   \nBut real general-purpose robots don’t exist yet\,  so we can’t collect real robot data from them. \nOne option is to bootstrap\, starting with specific tasks like driving or e-commerce package sorting\, where the objects vary but the task and environment don’t vary much\, and gradually expanding as specific skills are mastered into adjacent skills.  Some companies are developing such robots and putting them to work.   \nOne example is Waymo\, which has robot taxis operating in several US cities.  These robots have “level 4” autonomy – they rely on human operators who log in remotely to guide robot taxis when unfamiliar circumstances arise.   \nAnother example is Ambi Robotics\, which has package sorting and stacking machines operating in postal and warehouse facilities.  These robots are fully autonomous – but a few times an hour they drop a package.  As with Waymo\, human operators help out in such cases. \nBoth Waymo and Ambi have created a “data flywheel”\, where working robots constantly collect data that is used to improve robot performance and to enable adjacent robot skills\, like highway merging for Waymo and package stacking (very different from sorting) for Ambi. \nOne thing that Waymo and Ambi also have in common is that they don’t rely only on “end-to-end” AI models.  These companies combine advances in AI and learning from data with rigorous engineering methods like inverse kinematics\, 6d motion planning\, and digital signal processing. \nI call this GOFE (Good Old-Fashioned Engineering).  GOFE was developed long before modern AI.  GOFE is based on modularity\, metrics\, and step-by-step algorithms based on geometry and physics that can be fully understood and often guaranteed to perform reliably.  GOFE includes Kalman Filters\, RANSAC outlier rejection\, PID and MPC controllers\, etc [3].   \nWhereas “end-to-end” AI methods are “model-free”\, GOFE is model-based.  GOFE segments problems into modules\, so that each module can be tested\, fixed\, or fine-tuned independently\, and replaced when a better module becomes available. Model-free methods can be combined with model-based methods to “kickstart” robots to achieve the levels of reliability required for adoption in real commercial environments\, where they can then begin generating real robot data. I’ve been told such a combination is what’s behind the current success of Waymo\, and I know that a combination of model-free and GOFE is behind the success of Ambi.  Waymo’s robot taxis are collecting vast amounts of real data\, and  over the past 4 years\, Ambi has collected 22 years of real robot data as they have sorted over 100 million real packages [4]. \nAs noted at the beginning\, I don’t disagree with Rich Sutton – I believe that model-free AI will eventually surpass GOFE and that general-purpose robots will be common at some point in our future.  I look forward to that future and hope I get to see it.   \nBut when will the general-purpose robots arrive?  I’m not sure that the public (or investors) are willing to wait very long.  For the next few years\, the safest bet for closing the 100\,000 year data gap is to get real robots into production by combining GOFE with model-free methods.  These real robots can collect data as they perform useful work such as taxi driving and sorting packages.  That high-quality data will improve their performance and enable robots to perform adjacent skills\, spinning up the data flywheel until it collects enough data to enable general-purpose robots.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-good-old-fashioned-engineering-can-close-the-100000-year-data-gap-in-robotics/
LOCATION:Wu & Chen Auditorium
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
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