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    • Spring 2025 GRASP Seminar: Shubham Tulsiani, Carnegie Mellon University, “Reconstructing and Generating 3D”

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

    This will be a hybrid event with in-person attendance in Raisler Lounge (Towne 225) and virtual attendance on Zoom. ABSTRACT Modern reconstruction systems, leveraging advances in pose estimation and generative modeling, can reconstruct everyday objects and scenes from casually captured images. In this talk, I will highlight recent work from our group that pushes these […]

    MEAM Seminar: “Bioinspired Robotic Sensorimotor Systems via 3D Printed Soft and Architected Materials”

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

    Animals can easily adapt their bodies and movements to new, unstructured environments and situations. Robots cannot. While engineers construct robots from rigid, motorized mechanisms to precisely control their movements, vertebrates leverage compliant, deformable musculoskeletal systems to adaptively navigate complex environments and produce dynamically stable gaits and motions. Providing robots with an equivalent musculoskeletal system will […]

    ESE Spring Seminar – “Generalization, Memorization, and Privacy in Trustworthy Machine Learning”

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

    Machine learning is transforming numerous aspects of modern society, and its expanding use in high-stakes applications calls for responsible development. In this talk, I will present my research on the foundations and methodologies for building trustworthy ML, centered on three interconnected challenges: generalization, memorization, and privacy. First, I will show how information-theoretic tools can be […]

    CIS Seminar: “Probabilistic Experimental Design for Petascale DNA Synthesis”

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

    Generative modeling offers a powerful paradigm for designing novel functional DNA, RNA and protein sequences. In this talk, I introduce probabilistic experimental design methods to efficiently manufacture samples from generative models of biomolecules in the real world. These algorithms merge computational techniques for approximate sampling with physical randomness. I also develop tools to rigorously evaluate […]

    ASSET Seminar: “Algorithmic Stability for Trustworthy Machine Learning and Statistics”

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

    Abstract: Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them vulnerable to data poisoning attacks, prone to leaking sensitive information, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these […]

    Spring 2025 GRASP SFI: Student Lightning Talks, Session 2

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

    This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. ABSTRACT The Spring 2025 GRASP SFI Student Lightning Talks will highlight the research of three GRASP Lab Master's or early PhD students whose presentation topics have been nominated by their faculty advisors and voted on by their GRASP peers. […]

    The Harold Berger Distinguished Award Lecture 2025

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    Wu & Chen Auditorium

    Quantum Dots: From Curiosity to Technological Impact The combination of quantum effects, nanometer dimensions and a chemical synthesis make quantum dots a platform for exploring new size-dependent fundamental properties and a sandbox for developing new applications. This talk will cover the origin story of chemically synthesized quantum dots, their basic physics, the synthesis that unlocked […]

    MSE Seminar: “Soft Microparticle Assemblies to MAP Scaffolds” Tatiana Segura – Duke University

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    Wu & Chen Auditorium

    Particle assemblies form interconnected pockets of empty space that are hot spots for activity in many applications and natural phenomenon that deal with particulate matter. In my lab we explore the use of interlinked soft microparticle assemblies as scaffolds for cell culture in vitro and to promote regenerative wound healing in vivo. The open space that is created […]

    ESE Spring Seminar – “Can Robots Learn from Machine Dreams? – Robot Learning via GenAI-powered World Models”

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

    Over the past decade, large-scale pre-training followed by alignment has revolutionized natural language processing and computer vision. Yet, robotics remains constrained by the scarcity of real-world data. In this talk, I will present our systematic approach to overcoming this bottleneck by building increasingly rich world models from data. I will first introduce our distilled feature […]

    IDEAS/STAT Optimization Seminar: “Statistics-Powered ML: Building Trust and Robustness in Black-Box Predictions”

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

    Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: Modern ML models produce valuable predictions across various applications, influencing people’s lives, opportunities, and scientific advancements. However, these systems can fail in unexpected ways, generating unreliable inferences and perpetuating biases present in the data. These issues are particularly troubling in high-stakes applications, where models are trained on increasingly diverse, incomplete, and […]

    CIS Seminar: “AI for Materials Discovery: Graphs, Language Models, and Agents”

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

    Artificial intelligence (AI) is transforming scientific discovery, particularly in materials science, by accelerating the prediction and design of materials with desired properties. Traditional physics-based modeling of atomic systems is computationally prohibitive for large-scale problems, and AI addresses this challenge by learning the underlying physics from data, thereby accelerating discoveries. In this talk I will present […]