Events

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  • Oct01

    Engineering Career Services is excited to host our first Virtual Career Fair this Fall 2020, but we also recognize that this will be new for everyone involved. In this session we will go over virtual fair best practices and how to make the most of your virtual fair experience.

    11:00am to 4:00pm
    Online
    Engineering Career Services | Pomerantz Career Center
  • Oct02

    In this talk, I outline some of the challenges to securing elections in the United States, relying on my own research as well as recent reports of security incidents. I discuss two hot-button topics, ballot-marking devices and post-election audits, the challenges they present to election security, and what we as voters can do about it. I provide an overview of other threat surfaces to elections, and how they are defended (or not). I conclude with a discussion of what to look for in the days surrounding the November election and how to get involved in your local election process.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Matt Bernhard
    VotingWorks
  • Oct09

    Truly smart and responsive environments rely on the ability to detect physical events and social context, such as appliance use and human activities. Currently, to sense these types of events, one must either upgrade to “smart” appliances or attach aftermarket sensors to existing objects and infrastructure. These approaches are expensive, intrusive and inflexible. Furthermore, even "smart" appliances are often very dumb – a smart speaker sitting on a kitchen countertop cannot figure out if it is in a kitchen, let alone know the user is preparing dinner.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Chris Harrison
    Carnegie Mellon University
  • Nov06

    The Computer Science (CS) department welcomes prospective students interested in our graduate programs to our annual Prospective Student Visit Day on Fri, Nov 6.

    (All day)
    This event will be held remotely; details TBD
    UI CS Grad Students and Faculty
    University of Iowa Computer Science Department
  • Dec04

    Database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior. Setting these knobs correctly for an application's workload can improve the performance and efficiency of the DBMS. But such tuning requires considerable efforts from experienced administrators, which is not scalable for large DBMS fleets. This problem has led to research on using machine learning (ML) to devise strategies to optimize DBMS knobs for any application automatically.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Andy Pavlo
    Carnegie Mellon University
  • 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
  • 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
  • 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
  • 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
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