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    • ESE Seminar: “The Role of Explicit Regularization in Overparameterized Neural Networks”

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    Zoom - Email ESE for Link jbatter@seas.upenn.edu

    Overparameterized neural networks have proved to be remarkably successful in many complex tasks such as image classification and deep reinforcement learning. In this talk, we will consider the role of explicit regularization in training overparameterized neural networks. Specifically, we consider ReLU networks and show that the landscape of commonly used regularized loss functions have the […]

    ESE Seminar: “Learning is Pruning”

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    Zoom - Email ESE for Link jbatter@seas.upenn.edu

    The strong lottery ticket hypothesis (LTH) postulates that any neural network can be approximated by simply pruning a sufficiently larger network of random weights. Recent work establishes that the strong LTH is true if the random network to be pruned is a large poly-factor wider than the target one. This polynomial over-parameterization is at odds with […]

    Doctoral Dissertation: “Implantable Micro-Tissue Engineered Nerve Grafts to Maintain Regenerative Capacity and Facilitate Functional Recovery Following Nervous System Injury” (Justin Burrell)

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    The Department of Bioengineering at the University of Pennsylvania and Dr. D. Kacy Cullen are pleased to announce the Doctoral Dissertation Defense of Justin Burrell. Title:  Implantable Micro-Tissue Engineered Nerve Grafts to Maintain Regenerative Capacity and Facilitate Functional Recovery Following Nervous System Injury  The public is welcome to attend virtually via Bluejeans.

    MEAM Ph.D. Thesis Defense: “Structural and Mechanical Responses to Intermittent Parathyroid Hormone Treatment, Discontinuation, and Cyclic Administration Regimens”

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    Zoom - Email MEAM for Link peterlit@seas.upenn.edu

    Bone mineral density rapidly decreases upon withdrawal from intermittent parathyroid hormone (PTH) treatment despite its potent effect of promoting bone formation. To better understand this adverse phenomenon, this study first aimed to investigate the phenotype of PTH withdrawal in both intact and estrogen-deficient rat model by using a well-designed experiment combined with innovative longitudinal imaging […]

    • CBE PhD Dissertation Defense | Investigation of High-Surface-Area Titanate (ATiO3) Thin Films Prepared by Atomic Layer Deposition

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    Zoom - Email CBE for link

    Abstract:  "Heterogeneous catalysis is critically important in the chemical-processing and energy-related industries. Ideally, reaction sites on heterogeneous catalysts can be regenerated, and the catalysts should be active throughout the catalytic process. However, harsh reaction conditions and parallel side reactions can be harmful to the metal catalysts, making deactivation a common phenomenon. Finding a catalyst that […]

    CBE PhD Dissertation Defense | Study of Metal Catalysts Supported on Thin Films of Perovskites prepared by Atomic Layer Deposition (ALD)

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    Zoom - Email CBE for link

    Abstract:  Sintering is a severe problem with supported-metal catalysts in high-temperature applications, such as in automotive-emissions control, because it leads to a loss of catalytically active surface area. To stabilize the metal particles, in 2001, the Daihatsu group proposed using perovskite-supported metals, which they referred to as “intelligent” catalysts. The original goal was to regenerate […]

    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.