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Events for February 20, 2020
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CS Colloquium: Jiapeng Zhang (Harvard) - Sunflowers and Their Applications in Computer Science and Mathematics
Thu, Feb 20, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jiapeng Zhang, Harvard University
Talk Title: Sunflowers and Their Applications in Computer Science and Mathematics
Series: CS Colloquium
Abstract: The sunflower is a simple notion in combinatorics, originally invented and studied by Erdos and Rado in 1960. Surprisingly, it has deep connections to fundamental problems in computer science, such as matrix multiplication, efficient data structures, computational complexity and cryptography. In my talk, I will explain our new results on sunflowers, how ideas emerging from computer science were critical in the proof, and how our new techniques can help shed light on some central problems in computer science and mathematics.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Jiapeng Zhang is a postdoc at Harvard with Prof. Salil Vadhan. He did his PhD at UC San Diego with Prof. Shachar Lovett. His research focuses on boolean function analysis, computational complexity, learning theory and cryptography.
Host: Shaddin Dughmi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MASCLE Machine Learning Seminar: Rose Yu (Northeastern University) - Physics Guided AI for Learning Spatiotemporal Dynamics
Thu, Feb 20, 2020 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rose Yu, Northeastern University
Talk Title: Physics Guided AI for Learning Spatiotemporal Dynamics
Series: Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: Applications such as sports, climate science, and aerospace engineering require learning complex dynamics from large-scale spatiotemporal data. Such data is often non-linear, non-Euclidean, high-dimensional, and demonstrates complicated dependencies. Existing machine learning frameworks are still insufficient to learn spatiotemporal dynamics as they often fail to exploit the underlying physics principles. I will demonstrate how to inject physical knowledge in AI to deal with challenges such as non-linear dynamics, non-Euclidean geometry, and multi-resolution structure. I will showcase the application of these methods to problems such as accelerating turbulence simulations, imitating basketball gameplay and combating ground effect in quadcopter landing.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Yu is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University. Previously, she was a postdoctoral researcher at Caltech Computing and Mathematical Sciences. She earned her Ph.D. in Computer Sciences at the University of Southern California. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data, with a particular emphasis on physics-guided AI. Among her awards, she has won Google Faculty Research Award, the NSF CRII award, best dissertation award in USC, best paper award at the NeurIPS time series workshop, and was nominated as one of the 'MIT Rising Stars in EECS'.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
