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Events for March 31, 2020

  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Alan Liu (Carnegie Mellon University) - Enabling Future-Proof Telemetry for Networked Systems

    Tue, Mar 31, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Alan Liu, Carnegie Mellon University

    Talk Title: Enabling Future-Proof Telemetry for Networked Systems

    Series: CS Colloquium

    Abstract: Today's networked systems, such as data center, cellular, and sensor networks, face increasing demands on security, performance, and reliability. To fulfill these demands, we first need to obtain timely and accurate telemetry information about what is happening in the system. For instance, understanding the volume and the number of distinct network connections can help detect and mitigate network attacks. In storage systems, identifying hot items can help balance the server load. Unfortunately, existing telemetry tools cannot robustly handle multiple telemetry tasks with diverse workloads and resource constraints.

    In this talk, I will present my research that focuses on building telemetry systems that are future-proof for current and unforeseen telemetry tasks, diverse workloads, and heterogeneous platforms. I will discuss the efficient algorithms and implementations that realize this future-proof vision in network monitoring for hardware and software platforms. I will describe how bridging theory and practice with sketching and sampling algorithms can significantly reduce memory footprints and speedup computations while providing robust results. Finally, I will end the talk with new directions in obtaining future-proof analytics for other types of networked systems, such as low-power sensors and mobile devices, while enhancing energy efficiency and data privacy.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Alan (Zaoxing) Liu is a postdoctoral researcher at Carnegie Mellon University. His research interests are in networked and distributed systems with a recent focus on efficient system and algorithmic design for telemetry, big-data analytics, and privacy. His research papers have been published in venues such as ACM SIGCOMM, USENIX FAST, and OSDI. He is a recipient of the best paper award at USENIX FAST'19 for his work on large-scale distributed load balancing. His work received multiples recognitions, including ACM STOC "Best-of-Theory" plenary talk and USENIX ATC "Best-of-Rest". Prior to CMU, he obtained his Ph.D. in Computer Science from Johns Hopkins University.

    Host: Ramesh Govindan

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Undergraduate Admission Virtual Information Session

    Tue, Mar 31, 2020 @ 02:00 PM - 03:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for prospective first-year students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Please register here!

    Audiences: Everyone Is Invited

    Posted By: Viterbi Admission

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  • **CANCELED** ISE 651 - Epstein Seminar

    Tue, Mar 31, 2020 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Daniel W. Apley, Professor, Northwestern University

    Talk Title: TBD

    Host: Dr. Qiang Huang

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Posted By: Grace Owh

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Baharan Mirzasoleiman (Stanford University) - Efficient Machine Learning via Data Summarization

    Tue, Mar 31, 2020 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Baharan Mirzasoleiman, Stanford University

    Talk Title: Efficient Machine Learning via Data Summarization

    Series: CS Colloquium

    Abstract: Large datasets have been crucial to the success of modern machine learning models. However, training on massive data has two major limitations. First, it is contingent on exceptionally large and expensive computational resources, and incurs a substantial cost due to the significant energy consumption.

    Second, in many real-world applications such as medical diagnosis and self-driving cars, big data contains highly imbalanced classes and noisy labels. In such cases, training on the entire data does not result in a high-quality model. In this talk, I will argue that we can address the above limitations by developing techniques that can identify and extract the representative subsets from massive datasets. Training on representative subsets not only reduces the substantial costs of learning from big data, but also improves their accuracy and robustness against noisy labels. I will present two key aspects to achieve this goal: (1) extracting the representative data points by summarizing massive datasets; and (2) developing efficient optimization methods to learn from the extracted summaries. I will discuss how we can develop theoretically rigorous techniques that provide strong guarantees for the quality of the extracted summaries, and the learned models' quality and robustness against noisy labels. I will also show the applications of these techniques to several problems, including summarizing massive image collections, online video summarization, and speeding up training machine learning models.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Baharan Mirzasoleiman is a Postdoctoral Research Scholar in Computer Science Department at Stanford University, where she works with Prof. Jure Leskovec. Baharan's research focuses on developing new methods that enable efficient exploration and learning from massive datasets. She received her PhD from ETH Zurich, working with Prof. Andreas Krause. She has also spent two summers as an intern at Google Research. She was awarded an ETH medal for Outstanding Doctoral Dissertation, and a Google Anita Borg Memorial Scholarship. She was also selected as a Rising Star in EECS from MIT.

    Host: Bistra Dilkina

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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