Past events
ML Seminar: Shancong Mou
Tuesday, Sept. 17, 2024, 11 a.m. through Tuesday, Sept. 17, 2024, Noon
3-180 Keller Hall
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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 Fall 2024 semester.
This week's speaker, Professor Shancong Mou (Industrial and Systems Engineering, University of Minnesota), will be giving a talk titled "AI/ML-enabled Data Fusion for Complex Engineering Systems".
Abstract
Recent advancements in artificial intelligence (AI) and machine learning (ML), along with improvements in sensor technologies and computing power, have paved the way for data-driven solutions across a wide range of engineering applications. This talk explores novel applications of AI/ML methodologies for data analytics in complex engineering systems, highlighting the convergence of engineering science, optimization, and statistics.
I will start with an overview of the research landscape, followed by a discussion on AI/ML-enabled data fusion, demonstrated through several key examples:
Surface quality monitoring in personal electronics manufacturing, using robust learning for label-efficient monitoring of high-dimensional data.
Quality and productivity improvement in composite fuselage assembly, a critical process in modern airplane manufacturing, utilizing PDE-constrained optimization for design and optimal control.
Control and Design Optimization in Composite material and semiconductor manufacturing processes, leveraging physics-informed machine learning.
The talk will conclude with a discussion of current challenges and future research directions.
Biography
Shancong Mou is an assistant professor in the Department of Industrial and Systems Engineering at the University of Minnesota, Twin Cities. He received his Ph.D. in Industrial Engineering from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech in 2024. He also holds an MS in Computational Science and Engineering from Georgia Tech.
His research focuses on AI/ML-enabled data analytics for quality and productivity improvement in complex engineering systems, intersecting with statistics, operations research, machine learning, and computational science. His work is supported by NSF, Apple, Boeing, and OG Technologies. He has received numerous awards and scholarships from ASA, IISE, ISA, and Georgia Tech, including the Mary G. and Joseph Natrella Scholarship from ASA and the ISyE Outstanding Graduate Student Instructor of the Year award in 2022.
Graduate Programs Online Information Session
Wednesday, July 10, 2024, 10 a.m. through Wednesday, July 10, 2024, 11 a.m.
Virtual meeting - RSVP online
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:
2024 CS&E Undergraduate Student Graduation Event
Thursday, May 9, 2024, 9 a.m. through Thursday, May 9, 2024, 11 a.m.
University Recreation and Wellness Center - Beacon Room
123 SE Harvard St
Minneapolis, MN 55455
RSVP Link
Thursday, May 9th, 9 - 11 a.m.
University Recreation and Wellness Center - Beacon Room
Graduating undergraduate students and some of 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.
Caps and gowns are optional. We recommend dressing nicely if you plan on attending without a cap and gown.
Parking options - UMN Parking website
- Washington Avenue Parking Ramp
- University Avenue Parking Ramp
- Oak Street Ramp
- Maroon lot in the TCF Bank Stadium
College/University Commencement
2024 College of Science & Engineering Undergraduate Commencement
Thursday, May 9, 2024
3M Arena at Mariucci
Noon - Doors open to graduates and guests
1 p.m. - Ceremony begins
Ceremony will also be streamed live.
Formal Invitation (pdf)
Questions: csecommencement@umn.edu
Event website
2024 CS&E Graduate Student Graduation Event
Friday, April 26, 2024, 9 a.m. through Friday, April 26, 2024, 11 a.m.
University Recreation and Wellness Center - Beacon Room
123 SE Harvard St
Minneapolis, MN 55455
RSVP Link
Friday, April 26, 9 - 11 a.m.
University Recreation and Wellness Center - Beacon Room
All graduating students from the Computer Science, Data Science, and Bioinformatics and Computational Biology graduate programs, as well as 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.
Caps and gowns are optional. We recommend dressing nicely if you plan on attending without a cap and gown.
Parking options - UMN Parking website
- Washington Avenue Parking Ramp
- University Avenue Parking Ramp
- Oak Street Ramp
- Maroon lot in the TCF Bank Stadium
College/University Commencement
Master's and Doctoral Degree Student Commencement
Friday, April 26, 2024 - 2 p.m.
3M Arena at Mariucci
Questions: asecommencement@umn.edu
Event website
The Arts, Sciences, and Engineering Graduate Commencement is hosted by the College of Liberal Arts (CLA) and the College of Science and Engineering (CSE).
CLA and CSE master’s, doctoral, and postbaccalaureate certificate students are invited to attend the spring 2024 commencement. Eligible students will receive an email in early February with instructions regarding how to register and participate in the ceremony.
Spring 2024 Data Science Poster Fair
Friday, April 19, 2024, 10 a.m. through Friday, April 19, 2024, Noon
ABC room at the Campus Club in Coffman Memorial Union
We invite you to attend the Spring 2024 Data Science Poster Fair! This year's event will be held on April 19th from 10 am -12 pm in the ABC room at the Campus Club in Coffman Memorial Union.
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.
See below for this semester's presenters and capstone project topics.
Spring 2024 Posters
Jashwin Acharya Advisor: Wei Pan, School of Public Health "Use of a large language model for few-shot learning to predict dementia" | Aviral Bhatnagar Advisor: Jaideep Srivastava, Department of Computer Science and Engineering "Genome Sequencing" | Jiahao He Advisor: Erich Kummerfeld, Institute for Health Informatics "Identifying Health Condition Factors that Impact K-12 Education Outcomes" |
Jooyong Lee Advisor: Erich Kummerfeld, Institute for Health Informatics "Exploring Health-Related Determinants of Student's Academic Performance: A Causal Inference Approach Using the DoWhy Python Library" | Hahnemann Ortiz Advisor: Daniel Boley, Department of Computer Science and Engineering "Convergence of AI and DLT" | Jong Inn Park Advisor: Dongyeop Kang, Department of Computer Science and Engineering "Graphical Text Summarization Using Generative AI" |
Hari Veeramallu Advisor: Junaed Sattar, Department of Computer Science and Engineering "Study the feasibility of generating a top-down view of an Underwater Robot given an input stream from n RGB camera sensors." | Tianhong Zhang Advisor: Tianxi Li, School of Statistics "Comparative Analysis of Deep Learning and Stacking Methods for Link Prediction in Network Data" | Shifa Siddiqui Advisor: Rui Zhang, Department of Surgery "Leveraging Natural Language Processing to Analyze Healthcare Data" |
Venkata Sai Krishna Abbaraju Advisor: Jaideep Srivastava, Department of Computer Science and Engineering "Reviving lost data: Applying ML to impute missing data in factory datasets" | Dinesh Reddy Challa Advisor: William Northrop, Department of Mechanical Engineering "Influence of Snowfall on the Fuel Consumption of Winter Maintenance Vehicles" | Amrutha Shetty Jayaram Shetty Advisor: Dongyeop Kang, Department of Computer Science and Engineering "Bridging AI Dimensions: Small Model Precision Meets Large Model Depth in Therapy" |
Rahul Mehta Advisor: Erich Kummerfeld, Institute for Health Informatics "Transdiagnostic causal models of relationships among manic and depressive symptoms in mania, depression, mixed state, and euthymia" | Sam Penders Advisor: Vuk Mandic, School of Physics and Astronomy "LIGO All-Sky Long-Duration Transient Search Using Deep Learning" | Eric Trempe Advisor: Tianxi Li, School of Statistics "Predicting Patient Cancer Types Through Medical Measures" |
Keith Willard Advisor: Xiaotong Shen, School of Statistics "Using BART generative synthetic data to improve BERT parsing of patient prescription instructions." | Linjun Xia Advisor: Erich Kummerfeld, Institute for Health Informatics "A Correlation and Causality Study of Student Behavioral Conditions with Health and Achievement in Hopkins public schools" | SriHarshitha Anuganti Advisor: Rui Zhang, Department of Surgery "Development of dementia in patients who underwent bariatric surgery" |
Computer Science & Data Science Graduate Student Department Head Town Hall
Please join us at the Computer Science and Data Science Graduate Student Department Head Town Hall. Light refreshments and snacks will be available.
LOCATION: 3-180 (in-person only event; no Zoom stream)
This is your chance to voice your opinion and offer critical feedback on teaching, student services, and any other items you think can be improved. Your feedback and insights are important to help us improve your graduate experience.
Please use the link below to RSVP, or provide feedback regarding your experiences in your computer science courses and within the department. Please note that you can remain anonymous to provide feedback:
Computer Science & Data Science Undergraduate Student Department Head Town Hall
Please join us at the Computer Science and Data Science Undergraduate Student Department Head Town Hall. Light refreshments and snacks will be available.
This is your chance to voice your opinion and offer critical feedback on teaching, student services, and any other items you think can be improved. Your feedback and insights are important to help us improve your graduate experience.
Please use the link below to RSVP, or provide feedback regarding your experiences in your computer science courses and within the department. Please note that you can remain anonymous to provide feedback:
Graduate Programs Online Information Session
Monday, Feb. 12, 2024, 2:30 p.m. through Monday, Feb. 12, 2024, 3:30 p.m.
Virtual meeting - RSVP online
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:
Graduate Programs Online Information Session
Tuesday, Dec. 5, 2023, 2:30 p.m. through Tuesday, Dec. 5, 2023, 3:30 p.m.
Virtual meeting - RSVP online
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:
Fall 2023 Data Science Poster Fair
Saturday, Nov. 18, 2023, 9 a.m. through Saturday, Nov. 18, 2023, 2:30 p.m.
The Graduate Hotel
615 Washington Ave SE
Minneapolis, MN 55414
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
Aviral Bhatnagar Advisor: Jaideep Srivastava "Human-in-the loop Approach to enhance Urban Living" | Jingran Cao Advisor: Kristin Palmsten "Managing depression during pregnancy" | Colin Ornelas Advisor: Jie Ding "Using Machine Learning Methods to Predict Quarterback Fantasy Football Scores" |
Zhecheng Sheng Advisor: Serguei Pakhomov "Explore Gender Bias in Dementia Detection with BERT" | Jonah Shi Advisor: Sisi Ma "Applying machine learning methods to identify predictors and causes for trauma related outcomes" | Nicole Sullivan Advisor: Erich Kummerfeld "It was all YELLOW: predicting depression in teenage students using a bi-Yearly Ensemble Learner with an Optimization Workflow" |