Past events

We invite you to attend the Fall 2023 Data Science Poster Fair! This semester's event will be held in conjunction with the Department of Computer Science & Engineering's Research Showcase on Saturday, November 18 from 11:30 a.m. - 1 p.m. Learn more about the event.

As a part of their degree requirements, Data Science M.S. students conduct research under the direction of a faculty advisor culminating with a capstone project and poster presentation. Students presenting at this event will discuss details of their capstone project, including their driving question, methods, and results, and will be available to address questions and connect with guests during their time slot. Faculty from numerous departments across the University are affiliated with the data science graduate program, therefore a variety of areas are represented with each student’s capstone project. 

The CS&E Research Showcase is a bi-annual event that features the collective works of students and faculty in the Department of Computer Science & Engineering. The event will feature over 60 posters, as well as a keynote addresses from Eugene Spafford, the founder and executive director of the Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue University, and Ed Chi, CS&E alumni award winner and Distinguished Scientist at Google. 

This event is open to the public and all interested undergraduate and graduate students, alumni, staff, faculty, and industry professionals are encouraged to attend.  To let us know you'll be joining us, please fill out our RSVP form below. We ask those who plan to attend to RSVP by Friday, November 10. 
 

Fall 2023 Posters

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
  
   

Register now! (free)

Event Website

About the workshop

The goal of the workshop is to bring together experts working at the intersection of data science and materials science and explore promising data science approaches and techniques that could support major advances in materials science in the coming years. The registration is free and required. Lunch will be provided for registered attendees.

Speakers

The forum will include sessions and panel discussions led by experts from UMN, MIT, UIUC, UT Dallas, Argonne, NIST, Google, and NSF. The full schedule of the workshop can be found on the workshop website.

Poster session

Students and postdocs are encouraged to attend and to make contributions in the form of poster presentations (please submit an abstract on the registration form).

Organizing committee

Vuk Mandic, Chris Bartel, Sapna Sarupria, Ellad Tadmor, Ke Wang

For more information, please reach out to Prof. Vuk Mandic at vuk@umn.edu. 

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
  
   

RSVP Link
Thursday, May 11th, 2:00 pm - 4:00 pm
Coffman Memorial Union - Great Hall

All graduating undergraduate 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 - Questions about the University events (commencement@umn.edu)

Undergraduate Student Conferral Ceremony
Saturday, May 13, 2023 - 1 p.m.
Huntington Bank Stadium

Stage Crossings
Thursday, May 11–Saturday, May 13, 2023
University of Minnesota Field House

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.

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. 

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. Professional photographers will also be available to take photos.

UMN Commencement Page

Information on the CSE Commencement

Information on the CLA Commencement

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. 

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.