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Oct
20
Tue
CS Seminar Series: Jeremias Sulam, Johns Hopkins University, Johns Hopkins University – “Overparameterized and Adversarially Robust Sparse Models”
Oct 20 @ 10:45 am – 12:00 pm

Location

Zoom: https://wse.zoom.us/j/93897562229

Abstract

Sparsity has been a driving force in signal & image processing and machine learning for decades. In this talk we’ll explore sparse representations based on dictionary learning techniques from two perspectives: over-parameterization and adversarial robustness. First, we will characterizes the surprising phenomenon that dictionary recovery can be facilitated by searching over the space of larger (over-realized/parameterized) models. This observation is general and independent of the specific dictionary learning algorithm used. We will demonstrate this observation in practice and provide a theoretical analysis of it by tying recovery measures to generalization bounds. We will further show that an efficient and provably correct distillation mechanism can be employed to recover the correct atoms from the over-realized model, consistently providing better recovery of the ground-truth model.
We will then switch gears towards the analysis of adversarial examples, focusing on the hypothesis class obtained by combining a sparsity-promoting encoder coupled with a linear classifier, and show an interesting interplay between the flexibility and stability of the (supervised) representation map and a notion of margin in the feature space. Leveraging a mild encoder gap assumption in the learned representations, we will provide a bound on the generalization error of the robust risk to L2-bounded adversarial perturbations and a robustness certificate for end-to-end classification. We will demonstrate the applicability of our analysis by computing certified accuracy on real data, and comparing with other alternatives for certified robustness. This analysis will shed light on to how to characterize this interplay for more general models.

Bio

Jeremias Sulam is an assistant professor at the Biomedical Engineering department at JHU, and a faculty member of the Mathematical Institute for Data Science (MINDS) and the Center for Imaging Science (CIS). He received his PhD in Computer Science from the Technion-Israel Institute of Technology, in 2018. He is the recipient of the Best Graduates Award of the Argentinean National Academy of Engineering. His research interests include machine learning, signal and image processing, representation learning and their application to biomedical sciences.

Host

Department of Computer Science

Oct
27
Tue
Krasnopoler Lecture: Ed Catmull, Pixar Animation Studios and Pixar Animation and Disney Animation – “Q&A”
Oct 27 @ 7:00 pm – 8:30 pm

Location

Zoom link to be announced

Abstract

Ed Catmull will be hosting a live Q&A.

Bio

Ed Catmull, Turing Prize Award winner for his contributions to 3D graphics and CGI filmmaking.

Dr. Ed Catmull is co-founder of Pixar Animation Studios and the former president of Pixar, Walt Disney Animation Studios, and Disneytoon Studios. For over twenty-five years, Pixar has dominated the world of animation, producing fourteen consecutive #1 box office hits, which have grossed more than $8.7 billion at the worldwide box office to date, and won thirty Academy Awards®.

His book Creativity, Inc.—co-written with journalist Amy Wallace and years in the making—is a distillation of the ideas and management principles Ed has used to develop a creative culture. A book for managers who want to lead their employees to new heights, it also grants readers an all-access trip into the nerve center of Pixar Animation Studios—into the meetings, postmortems, and “Braintrust” sessions where some of the most successful films in history have been made.

Dr. Catmull has been honored with five Academy Awards��, including an Oscar of Lifetime Achievement for his technical contributions and leadership in the field of computer graphics for the motion picture industry. He also has been awarded the Turing Award by the world’s largest society of computing professionals, the Association for Computing Machinery, for his work on three-dimensional computer graphics. Dr. Catmull earned B.S. degrees in computer science and physics and a Ph.D. in computer science from the University of Utah. In 2005, the University of Utah presented him with an Honorary Doctoral Degree in Engineering. In 2018, Catmull announced his retirement from Pixar, though he has cemented his legacy as an innovator in technology, entertainment, business, and leadership.

Host

Department of Computer Science

Nov
10
Tue
CS Seminar Series: Mehran Armand, Johns Hopkins University APL, Johns Hopkins University APL – “TBA”
Nov 10 @ 10:45 am – 11:45 am

Location

Zoom: Link TBA

Abstract

TBA

Bio

TBA

Host

Department of Computer Science

Nov
17
Tue
CS Seminar Series: Homa Alemzadeh, Johns Hopkins University – “Context-Aware Safety Monitoring in Medical Cyber-Physical Systems”
Nov 17 @ 10:45 am – 12:00 pm

Location

Zoom

Bio

Homa Alemzadeh is an Assistant Professor in the Department of Electrical and Computer Engineering with a courtesy appointment in Computer Science at the University of Virginia. She is also a member of the Link Lab, a multi-disciplinary center for research and education in Cyber-Physical Systems (CPS). Before joining UVA, she was a research staff member at the IBM T. J. Watson Research Center. Homa received her Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign and her B.Sc. and M.Sc. degrees in Computer Engineering from the University of Tehran. Her research interests are at the intersection of computer systems dependability and data science, in particular data-driven resilience assessment and design of CPS with applications to medical devices, surgical robots, and autonomous systems. She is the recipient of the 2017 William C. Carter Ph.D. Dissertation Award in Dependability from the IEEE TC and IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance. Her work on the analysis of safety incidents in robotic surgery was selected as the Maxwell Chamberlain Memorial Paper at the 50th annual meeting of the Society of Thoracic Surgeons (STS) and was featured in the Wall Street Journal, MIT Technology Review, and BBC, among others.

Host

Department of Computer Science

Dec
1
Tue
Distinguished Lecturer: Daniela Rus, Massachusetts Institute of Technology – “TBA”
Dec 1 @ 10:45 am – 12:00 pm

Location

https://wse.zoom.us/j/96576244425

Abstract

TBA

Bio

TBA

Host

Department of Computer Science

Dec
8
Tue
CS Seminar Series: Iris Bahar, Brown University – “Accurate, Real-time Energy-efficient Scene Perception through Hardware Acceleration”
Dec 8 @ 10:45 am – 12:00 pm

Location

Zoom: https://wse.zoom.us/j/95216049470

Abstract

Technological advancements have led to a proliferation of robots using machine learning systems to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations of these systems. Despite the strengths of convolutional neural networks (CNNs) for object recognition, these discriminative techniques have several shortcomings that leave them vulnerable to exploitation from adversaries. In addition, the computational cost incurred to train these discriminative models can be quite significance. Discriminative-generative approaches offers a promising avenue for robust perception and action. Such methods combine inference by deep learning with sampling and probabilistic inference models to achieve robust and adaptive understanding. The focus is now on implementing a computationally efficient generative inference stage that can achieve real-time results in an energy efficient manner. In this talk, I will present our work on Generative Robust Inference and Perception (GRIP), a discriminative-generative approach for pose estimation that offers high accuracy especially in unstructured and adversarial environments. I will then describe how we have designed an all-hardware implementation of this algorithm to obtain real-time performance with high energy-efficiency.

Bio

  1. Iris Bahar received the B.S. and M.S. degrees in computer engineering from the University of Illinois, Urbana-Champaign, and the Ph.D. degree in electrical and computer engineering from the University of Colorado, Boulder. Before entering the Ph.D program at CU-Boulder, she worked for Digital Equipment Corporation on their microprocessor designs. She has been on the faculty at Brown University since 1996 and now holds a dual appointment as Professor of Engineering and Professor of Computer Science. Her research interest have centered on energy-efficient and reliable computing, from the system level to device level. Most recently, this includes the design of robotic systems. Recently, she served as the Program Chair and General Chair of the International Conference on Computer-Aided Design (ICCAD) in 2017, 2018 respectively and the General Chair of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) in 2019. She is the 2019 recipient of the Marie R. Pistilli Women in Engineering Achievement Award and the Brown University School of Engineering Award for Excellence in Teaching in Engineering. More information about her research can be found at http://cs.brown.edu/people/irisbahar

Host

Department of Computer Science

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