Past events

RSVP Link
Thursday, May 11th, 10:00 am - 12: 00 pm
Coffman Memorial Union - Great Hall

All graduating graduate students and their families and friends are invited to join the Department of Computer Science & Engineering in celebrating their accomplishments. This is a casual event to mingle with other graduates, take photos, and listen to speakers.  There will be light snacks and beverages at the event. This does not include a ceremony where names will be read and a stage crossing takes place.

College/University Commencement

UMN Commencement Website
Arts, Sciences, and Engineering Graduate Student Commencement

Graduate Student Conferral Ceremony
Friday, May 12, 2023 - 5 p.m.
Huntington Bank Stadium

The graduate ceremony will include master’s and doctoral degree students.

Stage Crossings
Thursday, May 11–Saturday, May 13, 2023

University of Minnesota Field House

Graduates will have the opportunity to sign up to cross a stage while their guests have a front-row viewing experience to cheer and take photos and video. CSE associate deans and other CSE faculty will be joining at multiple times during the stage crossings.

Graduates will choose a specific day and time where they will have their name announced, cross the stage, and be congratulated by a University leader. Graduates may choose to coordinate with their friends and colleagues to cross the stage sequentially. In addition, Ph.D. students may choose to invite their advisors and arrange to be hooded during their scheduled stage crossing time. Professional photographers will also be available to take photos. 

Registration for the Conferral Ceremonies and Stage Crossings is open until April 10, 11:59 p.m. Central Time.
Graduates should receive emails from Marching Order, our University vendor. If you have any technical issues with the registration site for stage crossings, please reach out to Marching Order tech help.

GradFest
Wednesday, March 22 and Thursday, March 23, 2023
10 a.m. – 5 p.m. each day
Coffman Memorial Union, Great Hall

Everything graduates need—all in one place.

Gradfest website

Diploma Covers will be distributed at the respective Huntington Bank Commencement event for all students in all colleges. For students that are not attending the general commencement and wish to pick up a cover from a main office, please contact CSE Student Services. 

Distinction Cords will be available to undergraduate students with qualifying GPAs. Contact CSE or CLA Student Services for more details. 

Every year, data science M.S. students present their capstone projects during this event as a part of their degree requirements. 

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.

For more information about each presenter, check out the detailed breakdown of each session.

10 am - 11 am - Session 1

11 am - 12 pm - Session 2

Please contact Allison Small at csgradmn@umn.edu with any questions.

RSVP today!.

During each session, the graduate staff will review:

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

Students considering the following programs should attend:

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

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Tuesday from 11 a.m. - 12 p.m. during the spring 2023 semester.

This week's speaker, Professor Haizhao Yang (University of Maryland), will be giving a talk titled "Finite Expression Method: A Symbolic Approach for Scientific Machine Learning".

Abstract

Machine learning has revolutionized computational science and engineering with impressive breakthroughs, e.g., making the efficient solution of high-dimensional computational tasks feasible and advancing domain knowledge via scientific data mining. This leads to an emerging field called scientific machine learning. In this talk, we introduce a new method for a symbolic approach to solve scientific machine learning problems. This method seeks interpretable learning outcomes in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX). It is proved in approximation theory that FEX can avoid the curse of dimensionality in discovering high-dimensional complex systems. As a proof of concept, a deep reinforcement learning method is proposed to implement FEX for learning the solution of high-dimensional PDEs and learning the governing equations of raw data.

Haizhao Yang's personal website

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 11 a.m. - 12 p.m. during the Fall 2022 semester.

This week's speaker, Yifan Peng (Cornell), will be giving a talk titled "Clinical natural language processing and deep learning in assisting medical image analysis".

Abstract

Medical imaging has been a common examination in daily clinical routines for screening and diagnosis of a variety of diseases. Although hospitals have accumulated a large number of image exams and associated reports, it is yet challenging to use them to build high-precision computer-aided diagnosis systems effectively. In this talk, I will present an overview of cutting-edge techniques for mining existing free-text report data to assist medical image analysis via natural language processing and deep learning. Specifically, I will discuss both pattern-based and machine learning-based methods to detect findings/diseases and their attributes (e.g., type, location, size) from the chest x-ray and CT reports. Using these methods, we can construct large-scale medical image datasets with rich information. I will also demonstrate three case studies of medical image analysis using these datasets: (i) common thorax disease detection and report generation from chest X-rays and (ii) lesion detection and segmentation from CT images.

Biography

Dr. Peng is an assistant professor at the Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis. Before joining Cornell Medicine, Dr. Peng was a research fellow at the National Center for Biotechnology Information (NCBI), the National Library of Medicine (NLM), National Institutes of Health (NIH). He obtained his Ph.D. degree from the University of Delaware. During his doctoral training, he investigated applications of machine learning in biomedical text-mining, with a focus on deep analysis of the linguistic structures of biomedical texts.

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 11 a.m. - 12 p.m. during the Fall 2022 semester.

This week's speaker, Zhengyuan Zhou (New York University Stern School of Business, Department of Technology, Operations and Statistics), will be giving a talk titled "Optimal No-Regret Learning in Repeated First-Price Auctions".

Abstract

First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms for display ads bidding. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction, where unlike in second-price auctions, it is no longer optimal to bid one's private value truthfully and hard to know the others' bidding behaviors?

In this talk, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions. We discuss our recent work in leveraging the special structures of the first-price auctions to design minimax optimal no-regret bidding algorithms. 

Biography

Zhengyuan Zhou is currently an assistant professor in New York University Stern School of Business, Department of Technology, Operations and Statistics. Before joining NYU Stern, Professor Zhou spent the year 2019-2020 as a Goldstine research fellow at IBM research. He received his BA in Mathematics and BS in Electrical Engineering and Computer Sciences, both from UC Berkeley, and subsequently a PhD in Electrical Engineering from Stanford University in 2019. His research interests lie at the intersection of machine learning, stochastic optimization and game theory and focus on leveraging tools from those fields to develop methodological frameworks to solve data-driven decision-making problems.

RSVP today!.

During each session, the graduate staff will review:

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

Students considering the following programs should attend:

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

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 11 a.m. - 12 p.m. during the Fall 2022 semester.

This week's speaker, Xiaoran Sun (FSS, UMN), will be giving a talk titled "Machine Learning for Human Development and Family Research: An Overview and an Example".

Abstract

This talk will first provide a brief overview about the utility of machine learning (ML) in research on developmental and family science by presenting what ML can offer in the face of theories and research questions in this field. Then the talk will introduce a study using a literature-driven supervised ML approach for empirical synthesis on how family experiences during adolescence predict future educational outcomes in adulthood. Based on the utility and the empirical synthesis example, there will be a discussion about future steps for how we can expand on the use of ML in social science research. Note that this talk will be focused on the applications of ML instead of technical details of advancing  ML itself. Questions, discussions, and comments will all be super appreciated given the project is still in its development stage.

Biography

Xiaoran Sun is an assistant professor in the Department of Family Social Science at the University of Minnesota. She is also a faculty affiliate of the Learning Informatics Lab in the College of Education and Human Development and of the Data Science Initiative. She obtained her PhD in Human Development and Family Studies from the Pennsylvania State University with an NSF traineeship on Big Data Social Science. Before joining UMN she was a postdoctoral scholar at Stanford University in the Departments of Pediatrics and Communication and a Stanford Data Science scholar. She uses ML in her research on family systems and adolescent development.

The speaker this week is Yunan Yang from ETH Zürich/Cornell. Yunan's research interests are in Numerical Analysis, Inverse Problems, Nonconvex Optimization, Optimal Transport, and Machine Learning. The title and abstract for her talk are below.

Title: Benefits of Weighted Training in Machine Learning and PDE-based Inverse Problems

You may attend the talk either in person in Walter 402 or register via Zoom. Registration is required to access the Zoom webinar.

Abstract: Many models in machine learning and PDE-based inverse problems exhibit intrinsic spectral properties, which have been used to explain the generalization capacity and the ill-posedness of such problems. In this talk, we discuss weighted training for computational learning and inversion with noisy data. The highlight of the proposed framework is that we allow weighting in both the parameter space and the data space. The weighting scheme encodes both a priori knowledge of the object to be learned and a strategy to weight the contribution of training data in the loss function. We demonstrate that appropriate weighting from prior knowledge can improve the generalization capability of the learned model in both machine learning and PDE-based inverse problems.