Past events
2024 CS&E Graduate Student Graduation Event
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
Speaker info
Name: Amrutha Shetty Jayaram Shetty
Looking ahead, I aim to transition into the role of a Data Analyst at Wunderlich Malec Engineering in Eden Prairie, Minnesota, post-graduation. I'm drawn to the vibrant culture and myriad opportunities that Minnesota offers, and I'm eagerly anticipating making it my home for the foreseeable future.
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.
Natural Language Processing Poster Presentations
We are going to hold two poster presentation sessions.
- April 23, 2:30-3:45pm
- April 25, 2:30-3:45pm
Spring 2024 Posters
Group A - April 25, 2:30-3:45pm
Lexical decryptors Sandeep Bhuiya, Mudit Jantwal, Hemanth Kumar Tirupati, Shreya Yashodhar "Scientific Text Simplification" | NLPros Benjamin Kosieradzki, Mitchell Kosieradzki "Evaluating the Boundary between In-Context Learning and Fine-Tuning" | pnlp fiction Rakshithaa Kanakarajan Selvarathinam, Vishal Kancharla, Sri Krishna Vamsi Koneru, Devansh Mishra "Identifying Bias in LLMs when using LRLs" |
Syntax Errors Clayton Carlson, Ryan Diaz, Charlie Rapheal, Sanjali Roy "Leveraging Language Models for Temporal Political Bias Analysis" | Tattered-animals Zheng Robert Jia, Brandon Nee, Andrei Solodin, Jack Swanberg "LLM Prompt Recovery" | WordWizards Morgan Bozeman, Tyler Cook, Connor Holm, Derek Wong "Performance of LLMs in Various Styles" |
EdgeCaseWizards Apekshik Panigrahi, Anna Terzian "Terraform - Automating Infrastructure as a Service" | Team bRockoLee Jithendra Jagannatha Kagathi, Arjun Thonoor, Anirudh Vasudevan, Ya-Hui Yang "Transpilation" | Team SOTA Evan Way, Jerry Yin, Zaifu Zhan, Zhongxing Zhang "PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant" |
Natural Language Processing Poster Presentations
We are going to hold two poster presentation sessions.
- April 23, 2:30-3:45pm (group B)
- April 25, 2:30-3:45pm (group A)
Spring 2024 Posters
Group B - April 23, 2:30-3:45pm
Caught with N-grams Michael Bronstein, Yichen Li, Lavanya Radhakrishnan, Wuhao Zhang "Automated Detection and Refutation of Climate-related Misinformation" | Cybertron Gehna Jain, Ryan Langman, Trae Primm, Swapnil Puranik "Evaluation of Knowledge Graphs in LLMs" | Fury GPT Nikil Krishnakumar, Sujeendra Ramesh, Rammesh Adhav Saravanan "User Friendly Drone Control: Fine-Tuning Language Models with ROS Commands for Real-World Application" |
NLP Ninjas Nirshal Chandra Sekar, Byeongchan Jeong, Fidan Mahmudova, Sheshasai Sairam "Human - Robot Interaction using LLM" | SpotHRI Adam Imdieke "A Language Interface for the Spot Robot" | Transformers Ritwick Banerjee, Madhan Mohan, Leyan Sayeh, Masha Volkova "Disease Diagnosis using LLM" |
NLPitch Dhondup Dolma, Jaeeun Lee, Yongtian Ou, Jiyoon Pyo "Transidiomation: Optimizing translation of idioms embedded in text" | PRWZ Zaccheri Ciampone, Wyatt Kormick, Raymond Lyon, Preston Zhu "Research Paper Simplification" | Too Long; Didn’t Read Ryan Johnsen, Dylan Paulson, Logan Schaaf, Tony Zhang "Cuisine Fusion Recipe Generato" |
ML Seminar: Beyond Adam: What Optimization Can Help Large Foundation Models
Beyond Adam: What Optimization Can Help Large Foundation Models
Spring 2024 Data Science Poster Fair
There will be two, one hour long sessions, and student presenters will only need to present for one of the two sessions.
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 "Data processing, and predictive and causal modeling, to describe and understand MN K-12 health and education outcome disparities in a local school district" |
Jooyong Lee Advisor: Erich Kummerfeld, Institute for Health Informatics "Causal inference to identify factors contributing to a decrease in student's GPA" | 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 "TBD" |
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 "Causal Discovery Analysis on Bipolar Disorder Patients" | 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" |
ML Seminar: Numerical understanding of neural networks: from representation to learning dynamics
Numerical understanding of neural networks: from representation to learning dynamics
CS-IDEA Self Defense Seminar
The CS-IDEA Committee is hosting a self-defense seminar for students, researchers, and staff in the Department of Computer Science & Engineering. There will be some physical contact with others during the seminar, mainly wrist grabs.
The event will be held on Monday, April 15 from 9-11 a.m. at the University Recreation and Wellness Center - Multipurpose Room 2.
Pizza and beverages will be provided at the conclusion of the event for participants. The event is free, but registration is required; please RSVP by Friday, April 12.
The Computer Science & Engineering (CS&E) department is committed to supporting and recruiting a diverse community of students, staff, and faculty and helping everyone in this community to thrive. This requires deliberate work to build an inclusive and supportive environment for those from historically underrepresented and non-traditional backgrounds. The Computer Science Inclusivity, Diversity, Equity, and Advocacy (CS-IDEA) committee aims to attract and retain diverse students, staff, and faculty in computer science and engineering and help all students, staff, and faculty thrive within the Department of Computer Science & Engineering at the University of Minnesota.
MSSE Information Session (Virtual)
Interested in learning more about the University of Minnesota's Master of Science in Software Engineering program?
Reserve a spot at an upcoming virtual information session to get all your questions answered.
Info sessions are recommended for those who have at least 1-2 years of software engineering experience.
During each session, MSSE staff will review:
- Requirements (general)
- Applying
- Prerequisite requirements
- What makes a strong applicant
- Funding
- Resources
- Common questions
- Questions from attendees
ML Seminar: Renbo Zhao
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 2024 semester.
This week's speaker, Renbo Zhao (University of Iowa), will be giving a talk.
CS&E Colloquium: Designing Algorithms for Massive Graph
The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Yu Chen (EPFL), will be giving a talk titled, "Designing Algorithms for Massive Graph".
Abstract
As the scale of the problems we want to solve in real life becomes larger, it is difficult to store the whole input or take a very long time to read the entire input. In these cases, the classical algorithms, even when they run in linear time and linear space, may no longer be feasible options as the input size is too large. To deal with this situation, we need to design algorithms that use much smaller space or time than the input size. We call this kind of algorithm a sublinear algorithm.
My primary research interest is in designing sublinear algorithms for combinatorial problems and proving lower bounds to understand the limits of sublinear computation. I also study graph sparsification problems, which is an important technique for designing sublinear algorithms on graphs. It is usually used as a pre-processing step to speed up algorithms.
In this talk, I’ll cover some of my work in sublinear algorithms and graph sparsifications. I’ll give more details on my recent works about vertex sparsifiers.
Biography
I'm a postdoc in the theory group at EPFL. I obtained my PhD from University of Pennsylvania, where I was advised by Sampath Kannan and Sanjeev Khanna. Before that, I did my undergraduate study at Shanghai Jiao Tong University. I’m a recipient of the Morris and Dorothy Rubinoff Award at University of Pennsylvania and the Best Paper award at SODA’19.