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Events for the 4th week of February

  • CS Colloquium: Cheng Tan (Courant Institute / New York University) - Auditing Outsourced Services

    Tue, Feb 18, 2020 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Cheng Tan, Courant Institute / New York University

    Talk Title: Auditing Outsourced Services

    Series: CS Colloquium

    Abstract: How can users of a cloud service verify that the service truly performs as promised? This question is vital today because clouds are complicated black boxes, running in different administrative domains from users. Their correctness can be undermined by internal corruptions---misconfigurations, operational mistakes, insider attacks, unexpected failures, or adversarial control at any layer of the execution stack.


    This talk will present verifiable infrastructure, a framework that lets users audit outsourced applications and services. I will introduce two systems: Orochi and Cobra, which verify the execution of, respectively, untrusted servers and black-box databases. Orochi and Cobra introduce various techniques, including deduplicated re-execution, consistent ordering verification, GPU accelerated pruning, and others. Beyond these two systems, I will also discuss verifiable infrastructure more generally.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Cheng Tan is a computer science Ph.D. candidate in the Courant Institute at New York University. His interests are in operating systems, networked systems, and security. His work on the Efficient Server Audit Problem was awarded best paper at SOSP 2017. His work on data center network troubleshooting at Microsoft Research has been deployed globally in more than 30 data centers in Microsoft Azure.

    Host: Barath Raghavan

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

    OutlookiCal
  • 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

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