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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.

    Kalona Brewing Company
    Denise Szecsei
    National Czech & Slovak Museum & Library
  • Mar11

    Given the increasing volume of sensitive data stored on systems that are connected to the internet, it is likely that cyber threats such as insider and ransomware will continue to be lucrative ammunitions for cybercriminals and cause billions of dollars of damage. While machine learning, more specifically, anomaly detection techniques can help us quickly detect and resolve such unexpected intrusions, critical applications such as health monitoring devices cannot directly leverage third-party anomaly detection services due to the sensitive nature of the data.

    4:00pm to 5:00pm
    218 MLH
    Shagufta Mehnaz
    Purdue University
  • Mar09

    Security and user privacy for complex networks and cyber-physical systems are often considered as afterthoughts. This leads to inadequate security evaluation early on the development cycle that fails to identify missing security and privacy guarantees in protocol designs. To make matters worse, unsafe practices and operational oversights stemming from unvetted simplification of complex protocol interactions further contribute to the deviation of deployments from designs.

    4:00pm to 5:00pm
    218 MLH
    Syed Rafiul Hussain
    Purdue University
  • Mar06

    This talk will introduce my research on type systems for higher-order programming languages, exemplified by some recent result on modularity and extensible data types.

    4:00pm to 5:00pm
    110 MLH
    J. Garrett Morris
    University of Kansas
  • Mar04
    6:30pm to 7:30pm
    30 SH
    SiTS - Students in Technology and Sciences
  • Feb28

    UICC 2020 logoAbstract: Networks not only appear in many high-impact application domains, but also have become an indispensable ingredient in a variety of data mining and machine learning problems. Often these networks are collected from different sources, at different time, at different granularities.

    Shambaugh Auditorium, Main Library (LIB)
    Hanghang Tong
    ACM University of Iowa Chapter
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