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  • Mar30

    The Internet of Things (IoT) heralds the emergence of multitudes of computing-enabled networked everyday devices with sensing capabilities in homes, cars, workplaces, and on our persons, leading to ubiquitous smarter environments and smarter cyber-physical “things.” The next natural step in this computing evolution is to develop the infrastructure needed for these computational things to collectively learn.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Shuochao Yao
    University of Illinois at Urbana-Champaign
  • Apr01

    When analyzing data sets from real-world applications, many classical "efficient" algorithms that run in time quadratic to the input size are considered too slow to be practical.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Hsien-Chih Chang
    Duke University
  • Apr03

    Recent innovations in artificial intelligence and machine learning not only excel at numerous tasks in industry and academia but also keep impacting our daily lives. With the power of large datasets and the availability of compute resources such as graphics processing units (GPUs) and tensor processing units (TPUs), machine learning models have grown with increasingly better performance. However, their success relies heavily on the surge of big computing and big data. There remain challenges emerging in the real world given limited resources.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Qian (Lara) Yang
    Duke University
  • Apr06

    Real-world graphs abound with structures: peer-to-peer networks have bounded growth; road networks are planar; social networks have small separators. How do we take these structures to algorithmic advantage?

    4:00pm to 5:00pm
    Zoom - See emails for details
    Hung Le
    University of Victoria
  • Apr08

    History on Tap - National Czech & Slovak Museum & Library - logoRobots have integrated into our society, moving beyond manufacturing applications. Robots can now be found working in hotels, hospitals, and schools. They are learning to drive and deliver packages. Robots also appear in television shows, films, and plays.

    6:30pm
    Kalona Brewing Company
    Denise Szecsei
    National Czech & Slovak Museum & Library

    https://www.ncsml.org/event/history-on-tap-humanoid-robot-takeover/

  • Mar27

    Design and operation of modern computer systems are increasingly influenced by external disciplines such as law and economics. In this talk, we explore two such emerging phenomena: digital privacy laws and cloud economics. I make a case for why these trends are fundamental and how they invalidate computing principles and practices that have years of precedence. Then, I demonstrate how to build systems infrastructure to manage their impact.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Supreeth Shastri
    University of Texas at Austin
  • Mar25
    6:30pm to 7:30pm
    Online
    SiTS - Students in Technology and Sciences

    https://sits.org.uiowa.edu/

  • Mar25

    Recovering data from noise is a fundamental problem that arises in many applications such as compressed sensing, channel communication, pattern matching, and internet advertising. The problem is known to be extremely difficult to solve with no additional information about the datasets. However, efficient recovery may be possible if the datasets have some specific pattern or structure associated with them. In this talk, I will present my work on the algorithmic aspects of recovering certain structured datasets from noise.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Venkata Gandikota
    TRIPODS - Institute for Theoretical Foundations of Data Science | University of Massachusetts Amherst
  • Mar25

    Postponed due to UI COVID-19 response - 

    One may still support our mission here!

    (All day)
    Online
    UI Dept of Computer Science | UI Center for Advancement

    https://1dayforiowa.org/computerscience20

  • Mar23

    Which locations and staff should we monitor in order to detect pathogen outbreaks in hospitals? How do we predict the peak intensity of the influenza incidence in an interpretable fashion? How do we infer the states of all nodes in a critical infrastructure network where failures have occurred? Leveraging domain-based information should make it possible to answer these questions. However, several new challenges arise such as (a) presence of more complex dynamics (b) data sparsity and (c) mismatch between data and the process.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Bijaya Adhikari
    Virginia Tech
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