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    • MEAM Seminar: “Towards the Discovery of Trustworthy and Interpretable ML-enabled Constitutive Laws for Solids from Low- and Limited-data”

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

    Machine learning techniques are gearing up to play a significant role in the field of computational solid mechanics and multiphysics, enabling the integration of experimental data and physical constraints towards data-driven constitutive laws, acceleration of computational techniques for multi-scale modeling, and new paradigms for the solution of forward and inverse problems, to name a few. […]

    CIS Seminar: ” How Algorithms Can Support Deliberative Democracy”

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

    Academics and political practitioners around the world are experimenting with a class of democratic innovations called deliberative mini-publics (DMs). In a DM, a panel of constituents convenes to deliberate about specific issues and make policy recommendations to traditional political decision-makers (e.g., legislators). Nearly all DMs rely on sortition – random selection – to choose the panelists. Sortition is often thought of as […]

    Spring 2024 GRASP SFI: Andrew Owens, University of Michigan, “Multimodal Learning from the Bottom Up”

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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 Today's machine perception systems rely extensively on supervision provided by humans, such as natural language. I will talk about our efforts to make systems that, instead, learn from two ubiquitous sources of unlabeled sensory data: visual motion […]

    ASSET Seminar: “Paths to AI Accountability” (Sarah Cen, Massachusetts Institute of Technology)

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

    ABSTRACT: In the past decade, we have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with […]

    CBE Seminar: “Molecular Microscopy with Single Cell Transcriptomic Data Resolves RNA Liquid Biopsies” (Sevahn Vorperian, Stanford)

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

    Abstract Invasive biopsy is the gold standard for diagnosing several diseases; however, these procedures offer a limited, localized view of the disease pathology to the physician and are not risk-free to the patient. Cell-free RNA (cfRNA) in blood plasma reflects dynamic gene expression changes and can facilitate early disease diagnosis, yet current cfRNA assays fall […]

    BE Seminar: “Mapping and engineering gene expression with chemical and spatial lenses” (Hailing Shi, Broad Institute & MIT)

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

    Precise RNA expression, tailored to specific brain regions, cell types, and subcellular compartments, is pivotal for orchestrating complex brain functions. In the first part of my talk, I will introduce a confocal imaging-based spatial transcriptomics platform, STARmap, that seamlessly combines in situ hybridization, hydrogel tissue chemistry, and in situ sequencing technologies. Leveraging scalable experimental and computational pipelines, […]

    CIS Seminar: “Rethinking Data Use in Large Language Models”

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

    Large language models (LMs) such as ChatGPT have revolutionized natural language processing and artificial intelligence more broadly. In this talk, I will discuss my research on understanding and advancing these models, centered around how they use the very large text corpora they are trained on. First, I will describe our efforts to understand how these […]

    Women in Data Science @ Penn

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    Jon M. Huntsman Hall 3730 Walnut Street, Philadelphia, PA, United States

    The Wharton School and Penn Engineering are proud to host the fifth annual Women in Data Science (WiDS) @ Penn Conference on February 8-9, 2024, on the University of Pennsylvania’s campus. A celebrated interdisciplinary event, WiDS @ Penn welcomes academic, industry, and student speakers from across the data science landscape to celebrate its diversity, both […]

    Spring 2024 GRASP on Robotics: Qixing Huang, University of Texas at Austin, “Geometric Regularizations for 3D Shape Generation”

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

    This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. ABSTRACT Generative models, which map a latent parameter space to instances in an ambient space, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic, which aligns the induced ambient distribution […]

    PICS Colloquium: “Wall-models of turbulent flows via scientific multi-agent reinforcement learning”

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

    The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics […]

    ESE Spring Seminar – “Physics-inspired Machine Learning”

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    Glandt Forum, Singh Center for Nanotechnology 3205 Walnut Street, Philadelphia, PA, United States

    Combining physics with machine learning is a rapidly growing field of research. Thereby, most work focuses on leveraging machine learning methods to solve problems in physics. Here, however, we focus on the converse, i.e., physics-inspired machine learning, which can be described as incorporating structure from physical systems into machine learning methods to obtain models with […]