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  • CS Colloquium: Anand Iyer (University of California, Berkeley) - Scalable Systems for Large-Scale Dynamic Connected Data Processing

    Mon, Mar 25, 2019 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Anand Iyer, University of California, Berkeley

    Talk Title: Scalable Systems for Large-Scale Dynamic Connected Data Processing

    Series: CS Colloquium

    Abstract: As the proliferation of sensors rapidly make the Internet-of-Things (IoT) a reality, the devices and sensors in this ecosystem-”such as smartphones, video cameras, home automation systems and autonomous vehicles-”constantly map out the real-world producing unprecedented amounts of connected data that captures complex and diverse relations. Unfortunately, existing big data processing and machine learning frameworks are ill-suited for analyzing such dynamic connected data, and face several challenges when employed for this purpose.

    In this talk, I will present my research that focuses on building scalable systems for dynamic connected data processing. I will discuss simple abstractions that make it easy to operate on such data, efficient data structures for state management, and computation models that reduce redundant work. I will also describe how bridging theory and practice with algorithms and techniques that leverage approximation and streaming theory can significantly speed up computations. The systems I have built achieve more than an order of magnitude improvement over the state-of-the-art and are currently under evaluation in the industry for real-world deployments.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Anand Iyer is a PhD candidate at the University of California, Berkeley advised by Prof. Ion Stoica. His research interest is in systems with a current focus on enabling efficient analysis and machine learning on large-scale dynamic, connected data. He is a recipient of the Best Paper Award at SIGMOD GRADES-NDA 2018 for his work on approximate graph analytics. Before coming to Berkeley, he was a member of the Mobility, Networking and Systems group at Microsoft Research India. He completed his M.S at the University of Texas at Austin.

    Host: Barath Raghavan

    Location: Ronald Tutor Hall of Engineering (RTH) - 115

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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