Past events

The application deadline for the computer science and data science majors is May 25.

Students typically apply to a major while enrolled in fall semester courses during their sophomore year (third semester).

Submit your application at the appropriate link below:

• CSE student application (Computer Science B.S. & Data Science B.S. programs)
• CLA student application (Computer Science B.A. programs)

All applicants will be notified of their admission decision via email within three weeks of the application deadline.

Prospective students can RSVP for an information session to learn about the following graduate programs:

• Computer Science M.S.
• Computer Science MCS
• Computer Science Ph.D.
• Data Science M.S.
• Data Science Post-Baccalaureate Certificate

During the information session, we will go over the following:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

The last day of the spring 2021 semester is Wednesday, May 12.

View the full academic schedule on One Stop.

Have all your questions about the Master of Science in Software Engineering (MSSE) program answered by attending this online information session.

RSVP now to reserve your spot.

Attendees will be sent a link prior to the event.
 

Final exams for spring 2021 will be held between Thursday, May 6 and Wednesday, May 12.

View the full academic schedule on One Stop.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's talk is a part of the Cray Distinguished Speaker Series. This series was established in 1981 by an endowment from Cray Research and brings distinguished visitors to the Department of Computer Science & Engineering every year.

Our speaker is Rebecca Willett from the University of Chicago.

Abstract

Many challenging image processing tasks can be described by an ill-posed linear inverse problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all lie in this framework. Recent advances in machine learning and image processing have illustrated that it is often possible to learn inverse problem solvers from training data that can outperform more traditional approaches by large margins. These promising initial results lead to a myriad of mathematical and computational challenges and opportunities at the intersection of optimization theory, signal processing, and inverse problem theory.

In this talk, we will explore several of these challenges and the foundational tradeoffs that underlie them. First, we will examine how knowledge of the forward model can be incorporated into learned solvers and its impact on the amount of training data necessary for accurate solutions. Second, we will see how the convergence properties of many common approaches can be improved, leading to substantial empirical improvements in reconstruction accuracy. Finally, we will consider mechanisms that leverage learned solvers for one inverse problem to develop improved solvers for related inverse problems.

This is joint work with Davis Gilton and Greg Ongie.

Biography

Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. Her research is focused on machine learning, signal processing, and large-scale data science. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group, received an Air Force Office of Scientific Research Young Investigator Program award in 2010, and was named a Fellow of the Society of Industrial and Applied Mathematics in 2021. She is a co-principal investigator and member of the Executive Committee for the Institute for the Foundations of Data Science, helps direct the Air Force Research Lab University Center of Excellence on Machine Learning, and currently leads the University of Chicago’s AI+Science Initiative. She serves on advisory committees for the National Science Foundation’s Institute for Mathematical and Statistical Innovation, the AI for Science Committee for the US Department of Energy’s Advanced Scientific Computing Research program, the Sandia National Laboratories Computing and Information Sciences Program, and the University of Tokyo Institute for AI and Beyond. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.

The last day of instruction for the fall 2020 semester is Monday, May 3.

View the full academic schedule on One Stop.

For this spring 2021 seminar series, GroupLens has invited the author of a recent human-computer interaction paper to come chat about their work.

• This week's paper: Women (Still) Ask For Less: Gender Differences in Hourly Rate in an Online Labor Marketplace
• Author: Eureka Foong, Postdoctoral Fellow, Tokyo College

Have all your questions about the Master of Science in Software Engineering (MSSE) program answered by attending this online information session.

RSVP now to reserve your spot.

Attendees will be sent a link prior to the event.
 

Please note that the Cray Distinguished Speaker Series featuring Anil Jain from Michigan State University has been postponed until Fall 2021.