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    • MSE Seminar: “The light stuff: sculpting photons at the molecular-scale for sustainability”

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    We present methods to sculpt light at the atomic and molecular scale to detect and control chemical transformations, en-route to improved planetary and personal health. First, we study plasmon-driven chemical transformations, focussing on the photocatalytic dehydrogenation of AuPd systems. Here, the Au acts as a plasmonic light absorber and Pd serves as the catalyst. Using […]

    Doctoral Dissertation Defense: “Multiscale Modeling of Cell Fate Switching to Predict Patient-specific Responses to Combination Cancer Therapy” (Lindsey Fernandez)

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    The Department of Bioengineering at the University of Pennsylvania and Dr. Ravi Radhakrishnan are pleased to announce the Doctoral Dissertation Defense of Lindsey Fernandez. The public is welcome to attend via Blujeans (Meeting ID 680 058 608 4). Title: "Multiscale Modeling of Cell Fate Switching to Predict Patient-specific Response to Combination Cancer Therapy"

    CIS Seminar: “Hash Tables Lecture”

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    Zoom - Email CIS for link cherylh@cis.upenn.edu

    Abstract: The lecture that I will cover is going to be on Hash Tables. The lecture is designed for an introductory CS course and/or a data structures course. We are in the last 3rd of the semester when this topic is covered. At this point, students are comfortable with programming (in Python). In addition, they […]

    BE Doctoral Dissertation Defense: “Magnetic Resonance Imaging Assessment of Maternal Uteroplacental Hemodynamics During Pregnancy” (Eileen Hwuang)

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    The Department of Bioengineering along with Drs. Walter Witschey and John Detre are pleased to announce the Doctoral Dissertation Defense of Eileen Hwuang. The public is welcome to attend via Zoom. Title: "Magnetic resonance imaging assessment of maternal uteroplacental hemodynamics during pregnancy" Zoom link Meeting ID: 745 723 0989 Passcode: 526101

    2020 Heilmeier Award Lecture, Dr. Dan Roth

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    Abstract: The fundamental issue underlying natural language understanding is that of semantics – there is a need to move toward understanding natural language at an appropriate level of abstraction in order to support natural language understanding and communication with computers.

    Machine Learning has become ubiquitous in our attempt to induce semantic representations of natural language and support decisions that depend on it; however, while we have made significant progress over the last few years, it has focused on classification tasks for which we have large amounts of annotated data. Supporting high level decisions that depend on natural language understanding is still beyond our capabilities, partly since most of these tasks are very sparse and generating supervision signals for it does not scale.

    I will discuss some of the challenges underlying reasoning – making natural language understanding decisions that depend on multiple, interdependent, models, and exemplify it using the domain of Reasoning about Time, as it is expressed in natural language.

    BE Seminar: Deconstructing and Reconstructing Human Tissues (Kelly Stevens)

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    This seminar will be held virtually on Zoom. Check email for details or contact ksas@seas.upenn.edu. Although much progress has been made in building artificial human tissues over the past several decades, replicating complex tissue structure remains an enormous challenge. To overcome this challenge, our field first needs to create better three-dimensional spatial maps, or “blueprints” […]

    GRASP On Robotics: “Biorobotics for Personal Assistance – Translational Research and Opportunities for Human-Centered Developments”

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    https://upenn.zoom.us/j/96715197752

    Abstract: The seminar will focus on the opportunities and challenges offered by the digital transformation of healthcare which was accelerated in the COVID-19 Pandemia. In this framework rehabilitation and social robotics can play a fundamental role as enabling technologies for providing innovative therapies and services to patients even at home or in remote environments. In […]

    PICS Colloquium: “Machine learning for Fluid Mechanics”

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    Abstract: Many tasks in fluid mechanics, such as design optimization and control, are challenging because fluids are nonlinear and exhibit a large range of scales in both space and time. This range of scales necessitates exceedingly high-dimensional measurements and computational discretization to resolve all relevant features, resulting in vast data sets and time-intensive computations. Indeed, […]