Upcoming events

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

This week's talk is 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. 

This week's speaker is Jelani Nelson from the University of California, Berkeley.

Abstract

A "sketch" is a data structure supporting some pre-specified set of queries and updates to a database while consuming space substantially (often exponentially) less than the information theoretic minimum required to store everything seen, and thus can also be seen as some form of functional compression. A "streaming algorithm" is simply a data structure that maintains a sketch dynamically as data is updated. The advantages of sketching include less memory consumption, faster algorithms, and reduced bandwidth requirements in distributed computing environments. Despite decades of work in the area, some of the most basic questions still remain open or were only resolved recently. In this talk, I survey recent results across a variety of sketching topics.

Biography

Jelani Nelson is Professor in the Department of EECS at UC Berkeley. His research interests include sketching and streaming algorithms, dimensionality reduction, compressing sensing, and randomized linear algebra. He has been a recipient of the PECASE award, a Sloan Research Fellowship, and an NSF CAREER award. He is also the Founder and President of a 501(c)(3) nonprofit, "AddisCoder Inc.", which organizes annual summer camps that have provided algorithms training to over 500 high school students in Ethiopia.

https://people.eecs.berkeley.edu/~minilek/

We invite you to attend the annual Data Science Poster Fair! This year's event will be held virtually via Zoom on Friday, April 23 from 11:30 a.m. - 1:00 p.m.

Every year, data science M.S. students present their capstone projects during this event. This year, research preview videos will be posted to this page a week in advance, so attendees can view and plan their participation during the virtual event.

The poster fair is open to the public and all interested undergraduate and graduate students, alumni, staff, faculty, and industry professionals are encouraged to attend.

More details will be posted as they are available on the data science website.

Research shows that “LGBTQ+ persons [are] not only more likely than their otherwise similar non-LGBTQ+ peers to experience social marginalization and harassment in their workplaces, but [are] also more likely to report limited career opportunities and to have had their professional expertise devalued by their colleagues (Cech & Waidzunas, 2021). Additionally, according to a report conducted by the United Nations Educational, Scientific, and Cultural Organization (UNESCO), “while there will be 7 million new STEM jobs by 2025 and not enough people to fill them, only 35% of higher education students studying STEM subjects are women. It is clear that despite the increase in employment within STEM, women are still less likely to fill up these jobs.” (Li, 2019).

This panel is to present tech-interested students who hold marginalized gender and sexual identities with unique perspectives on how to leverage said identities during the career planning process. We know some students may view their identities as a barrier to success, but these identities can also be an asset. On this panel, several UMN grads will share how they have leveraged their unique identities and experiences to create successful careers and advance equity along the way.

RSVP at z.umn.edu/identityintech

Hosted by: CLA Career Services and Computer Science & Engineering Undergraduate Programs

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

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.
 

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

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

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.

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

View the full academic schedule on One Stop.