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  • Past Events

  • Dec01

    This lecture by CS Professor Aaron Stump is part of the Fall 2015 CLAS Master Class, entitled "What We Talk About When We Talk About Love" which brings together some of the College’s best teachers, each of whom will lecture on a single topic, demonstrating how varied experts would differently approach the subject at hand (love!). Students will gather for weekly lectures in the majestic auditorium of Macbride Hall, home of the UI Natural History Museum, and one of the most historic buildings on campus.

    5:00pm to 6:15pm
    MH AUD
    Aaron Stump
    College of Liberal Arts and Sciences
  • Dec04

    Program synthesis refers to the problem of constructing programs from some kind of specification. In this talk, we discuss two automatic program synthesis techniques. First, we ask the question, how can we synthesize a recursive program from simple examples describing its inputs and outputs? We present a technique that inductively generalizes from the examples to arrive at a recursive function. The tool we will discuss was able to synthesize a range of programs, from integer- to tree-manipulating recursive programs.

    4:00pm to 5:00pm
    110 MLH
    Aws Albarghouthi
    Department of Computer Sciences | University of Wisconsin-Madison
  • Dec07

    The Computer Science, Mathematics, and Statistics Departments are offering a new Certificate on Big Data Analysis starting this Fall (2015).


    12:00pm to 1:00pm
    UCC 2390
  • Nov20

    In this talk, we explore the performance limits of recovering structured signals from low-dimensional linear projections, using tools from high dimensional convex geometry. In particular, we focus on two signal reconstructions: a total variation minimization for recovering gradients-sparse signals and a low-rank Hankel matrix completion for super-resolution of spectrally sparse signals. Using the tool of Gaussian width, we obtain counter-intuitive performance bounds on the sample complexity for these two applications.

    4:00pm to 5:00pm
    110 MLH
    Weiyu Xu
    Department of Electrical and Computer Engineering | University of Iowa
  • Nov13

    In this talk, I’ll introduce my research on descriptive and predictive analytics for complex social networks from three perspectives:

    1. individual behaviors in online social networks;
    2. network dynamics such as diffusion and robustness; and
    3. the application of network analytics in the studies of teams and organizations.


    To support my research, I used various computational and quantitative methods, including network analysis and modeling, data and text mining, agent-based simulations, optimization, and statistical analysis.

    4:00pm to 5:00pm
    110 MLH
    Kang Zhao
    Management Sciences Dept, University of Iowa
  • Nov12

    Meeting will start at 7 pm and include a Google hangout with three  engineers at 7:30 pm. They will be sharing valuable information not just about Pinterest but also on how to get noticed by a company you want to work for - especially when that company doesn't recruit at your school.

    7:00pm to 8:30pm
    110 MLH
    Women in Informatics and Computer Science (WICS)
  • Nov06

    At the 1st Iowa Computer Science Graduate Research Symposium (2015) senior CS PhD students will present talks on their latest research, showcasing a variety of CS research areas including algorithms, mobile computing, networks, programming languages, and virtual reality. Talks are intended for a wide audience with interest in CS, including CS juniors and seniors.

    9:00am to 5:30pm
    MLH and W401 PBB
    UI CS Grad Students and Faculty
    University of Iowa Computer Science Department
  • Nov04

    Google is reaching out to UI technical students virtually via Hangouts! Want to hear about what it’s like to work at Google? Want to gain tips for your resume and Google interviews? Come learn firsthand from a Google engineer! We’ll also share info on some of our internship and job opportunities for technical students.


    RSVP so Google can contact you about opportunities!

    5:30pm to 7:00pm
    110 MLH
    ACM University of Iowa Chapter
  • Oct30

    Many ranking algorithms are implemented based sequentially comparing pairs of items. However, in many situations where the ranking is based on subjective criterions, human intelligence is often needed to conduct pairwise comparison and provides more accurate and robust result than machine intelligence. The traditional approach of comparing pairs of items by a small group of expert is too slow, expensive, and cannot meet the growing needs for training data.

    4:00pm to 5:00pm
    110 MLH
    Qihang Lin
    Management Sciences Dept | Tippie College of Business | Univ of Iowa
  • Oct29

    If you've never carved pumpkins before, don't worry!  The pumpkin carving experts in our midst can show you how it's done.  And for those of you who think your pumpkin carving skills are pretty awesome -- let's see those skills in action!  We’ll have patterns for all skill levels so don't be shy!  And food/treats, too, of course. 

    Please RSVP to Catherine ( by Tuesday, Oct. 27, if you plan to participate, just to be sure we have enough pumpkins.

    6:30pm to 7:30pm
    Muhly Lounge (3 MLH)
    Baba Yaga
    Women in Informatics and Computer Science (WICS)
  • Oct23

    In this talk we present a powerful algorithmic framework for large-scale optimization, called Block Successive Upper bound Minimization (BSUM). The BSUM includes as special cases many well-known methods for signal processing, communication or massive data analysis, such as Block Coordinate Descent (BCD), Convex-Concave Procedure (CCCP), Block Coordinate Proximal Gradient (BCPG) method, Nonnegative Matrix Factorization (NMF), Expectation Maximization (EM) method and so on.

    4:00pm to 5:00pm
    110 MLH
    Mingyi Hong
    Iowa State University
  • Oct21

    HackerRank/technical interview questions practice.

    Muhly Lounge - 3 MLH
    ACM University of Iowa Chapter
  • Oct16

    In this talk I will discuss examples of how Amazon serves customers and improves efficiency using learning algorithms applied to large-scale datasets. I’ll explain the Amazon approach to projects in data science, which is based on applying tenets that are beneficial to follow outside the company as well as inside it.

    4:00pm to 5:00pm
    110 MLH
    Charles Elkan


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