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Events for April 07, 2022
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CS Undergraduate Web Registration Live Chat AssistanceThu, Apr 07, 2022 @ 09:00 AM - 09:30 AM
Thomas Lord Department of Computer Science
Student Activity
If you are a CS undergraduate with a web registration permit time of 9am today and are having difficulty with web registration, the advisement staff will be available from 9:00am - 9:30am to help troubleshoot your registration questions and issues. Chat with us at https://www.cs.usc.edu/chat/
Audiences: Undergrad
Contact: USC Computer Science
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CS Colloquium: Geoff Pleiss (Columbia University) - Bridging the Gap Between Deep Learning and Probabilistic Modeling
Thu, Apr 07, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Geoff Pleiss , Columbia University
Talk Title: Bridging the Gap Between Deep Learning and Probabilistic Modeling
Series: CS Colloquium
Abstract: Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify uncertainty - necessary for safety-critical applications and downstream decision-making tasks. I will discuss examples from my research that bridge the gap between these two learning paradigms. The first half will show that insights from deep learning can improve the practicality of probabilistic models. I will discuss work that scales Gaussian process regression, a common probabilistic model, to datasets two orders of magnitude larger than previously reported. The second half will show that probabilistic methods can improve our understanding of deep learning. I will demonstrate that Gaussian process theory uncovers new insights about the effects of width and depth in neural networks. I will conclude with ongoing efforts to quantify neural network uncertainty, develop new inductive biases, and other work at the intersection of deep learning and probabilistic modeling.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Geoff Pleiss is a postdoctoral researcher at Columbia University, hosted by John Cunningham, with affiliations in the Department of Statistics and the Zuckerman Institute. He obtained his Ph.D. in Computer Science from Cornell University, advised by Kilian Weinberger, and his B.Sc. from Olin College of Engineering. His research interests are broadly situated in machine learning, including neural networks, Gaussian processes, uncertainty quantification, and scalability. Geoff is also the co-founder and maintainer of the GPyTorch software framework.
Host: Robin Jia
Location: Olin Hall of Engineering (OHE) - 132
Audiences: By invitation only.
Contact: Assistant to CS chair
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DEI Committee Meeting
Thu, Apr 07, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly DEI Committee meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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CS Colloquium: Hussein Sibai (UC Berkeley) - Towards Physics-aware Trustworthy Autonomy
Thu, Apr 07, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Hussein Sibai , UC Berkeley
Talk Title: Towards Physics-aware Trustworthy Autonomy
Series: CS Colloquium
Abstract: Designing trustworthy autonomous systems is a looming challenge in several domains. Symbolic reasoning and verification can complement purely data-driven approaches by exploiting knowledge of structure and code, providing rigorous safety assurances, explaining why designs work, and helping find edge-cases quickly. In this talk, I will discuss recent results that use knowledge about physical laws, such as symmetries, to boost the scalability of formal verification of autonomous systems. The boosting benefits both data-driven and model-based analysis. My tool SceneChecker embodies these algorithms and data structures that use knowledge of symmetries to save verification algorithms from repeating expensive reachability computations. It implements a counterexample-guided abstraction-refinement (CEGAR) verification algorithm that compresses models by combining symmetric states. SceneChecker has been successful in verifying complex scenarios involving ground and aerial vehicles. In the second half, I will present results developed using notions from topological entropy to relate knowledge of physical laws governing a system with data requirements in solving estimation and verification problems. These results can give physics-aware lower-bounds that can guide future autonomy design processes.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Hussein Sibai is a Postdoctoral Scholar at UC Berkeley, advised by Murat Arcak and Sanjit Seshia. He obtained his Ph.D. in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign (UIUC) in December 2021, advised by Sayan Mitra. He received his bachelor's degree in Computer and Communication Engineering from the American University of Beirut and a master's degree in Electrical and Computer Engineering from UIUC. His research interests are in formal methods, control theory, and machine learning. Hussein has won the best poster award in HSCC 2018 and best paper nominations at HSCC 2017 and ATVA 2019. His work has been recognized by the Rambus fellowship, the Ernest A. Reid fellowship, the MAVIS Future Faculty fellowship, and the ACM SIGBED gold medal for the graduate category in the student research competition in CPS Week 21.
Host: Jyo Deshmukh
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: By invitation only.
Contact: Assistant to CS chair
