ESE Seminar: “New Tools for Better Understanding Social Networks”

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

We shall examine in our talk concepts and tools for the analysis of social networks. We shall present in particular YouTube and Twitter. For YouTube, we point at shortcomings in ways to measure audience retention and propose new concepts to better quantify desirable properties. We then present a geo-linguistic analysis of Twitter based on daily […]

ESE Seminar: “Accelerating MRI with Deep Learning”

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

Magnetic Resonance Imaging (MRI) can be accelerated by sampling below the Shannon-Nyquist rate via compressed sensing techniques. In this talk, I will consider the problem of optimizing the under-sampling pattern in a data-driven fashion, which has been an open problem for over a decade. For a given sparsity constraint, our method optimizes the under-sampling pattern […]

CBE PhD Dissertation Defense | Polymer Mechanics and Dynamics in Polymer Nanoparticle Composites

Zoom - Email CBE for link

Abstract:  "Polymer nanoparticle composites (PNCs) have become an important topic of research due to their highly tunable macroscopic properties. Compared to the pure polymers, PNCs exhibit increase in mechanical strength, altered thermodynamic properties, and simultaneous improvement in permeability and selectivity in small molecule transport. Understanding the fundamental physics that control the behavior of both components […]

ESE Grace Hopper Lecture: “Scalable Photonics: An Optimized Approach”

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

Classical and quantum photonics with superior properties can be implemented in a variety of old (silicon, silicon nitride) and new (silicon carbide, diamond) photonic materials by combining state of the art optimization and machine learning techniques (photonics inverse design) with new fabrication approaches. In addition to making photonics more robust to errors in fabrication and […]

Doctoral Dissertation Defense: “Light-inducible control of microtubule organization in minimal cell-like compartments” (Jessica Bermudez)

The Department of Bioengineering at the University of Pennsylvania and Dr. Matthew Good are pleased to announce the Doctoral Dissertation Defense of Jessica Bermudez. The public is welcome to attend the Zoom meeting via the details below. Title: Light-inducible control of microtubule organization in minimal cell-like compartments Date: Wednesday, November 11th, 2020 Time: 12:30 PM EST Join Zoom Meeting […]

ESE Seminar: “A New Era of Open-Source System-on-Chip Design”

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

Open-source software has been a critical enabler for tremendous innovation in the software ecosystem over the past two decades. Inspired by this success, open-source hardware involves making the high-level description of hardware components freely available for others to study, change, distribute, and ultimately use in fabricating their own hardware components. Unfortunately, open-source hardware has had […]

ESE Seminar: “Quantum Dot Plasmon Nanolasers”

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

Miniaturized light sources are critical for next-generation on-chip photonic devices. Plasmon-based lasers and surface plasmon amplified spontaneous emission of radiation (spasers) have received significant attention since their prediction over a decay over a decade ago. Major advances have included subwavelength footprint sizes, room-temperature operation, far-field emission directionality, and understanding of the lasing mechanism. Notably, one […]

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

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”

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 […]