Past events

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

Group A posters

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

Full poster details

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.  

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
   

RSVP for the next information session now

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.

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.

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Caiwen Ding (University of Connecticut), will be giving a talk titled, "Co-Designing Algorithms and Hardware for Efficient Machine Learning (ML): Advancing the Democratization of ML". 

Abstract

The rapid deployment of ML has witnessed various challenges such as prolonged computation and high memory footprint on systems. In this talk, we will present several ML acceleration frameworks through algorithm-hardware co-design on various computing platforms. The first part presents a fine-grained crossbar-based ML accelerator. Instead of attempting to map the trained positive/negative weights
afterwards, our key principle is to proactively ensure that all weights in the same column of a crossbar have the same sign, to reduce area. We divide the crossbar into sub-arrays, providing a unique opportunity for input zero-bit skipping. Next, we focus on co-designing Transformer architecture, and introduce on-the-fly attention and attention-aware pruning to significantly reduce runtime latency. Then, we will focus on co-design graph neural network training. To explore training sparsity and assist explainable ML, we propose a hardware friendly MaxK nonlinearity, and tailor a GPU kernel. Our methods outperform the state-of-the-arts on different tasks. Finally, we will discuss today's challenges related to secure edge AI and large language models (LLMs)-aided agile hardware design, and outline our research plans aimed at addressing these issues.

Biography

Caiwen Ding is an assistant professor in the School of Computing at the University of Connecticut (UConn). He received his Ph.D. degree from Northeastern University, Boston, in 2019, supervised by Prof. Yanzhi Wang. His research interests mainly include efficient embedded and high-performance systems for machine learning, machine learning for hardware design, and efficient privacy-preserving machine learning. His work has been published in high-impact venues (e.g., DAC, ICCAD, ASPLOS, ISCA, MICRO, HPCA, SC, FPGA, Oakland, NeurIPS, ICCV, IJCAI, AAAI, ACL, EMNLP). He is a recipient of the 2024 NSF CAREER Award, Amazon Research Award, and CISCO Research Award. He received the best paper nomination at 2018 DATE and 2021 DATE, the best paper award at the DL-Hardware Co-Design for AI
Acceleration (DCAA) workshop at 2023 AAAI, outstanding student paper award at 2023 HPEC, publicity paper at 2022 DAC, and the 2021 Excellence in Teaching Award from UConn Provost. His team won first place in accuracy and fourth place overall at the 2022 TinyML Design Contest at ICCAD. He was ranked among Stanford’s World’s Top 2% Scientists in 2023. His research has been mainly funded by NSF, DOE,
DOT, USDA, SRC, and multiple industrial sponsors.

Join the Department of Computer Science & Engineering (CS&E) for this all-alumni event to discuss AI in health and medicine, featuring Chad Myers, Ju Sun, Yogatheesan Varatharajah, and Qianwen Wang. Enjoy hosted beverages and appetizers, and the chance to reconnect with former classmates, colleagues, instructors, and friends. All alumni of the University of Minnesota CS&E programs (Computer Science, Data Science, MSSE) are invited to attend, and guests are welcome. 

There is no charge to attend our event, but pre-registration is required. 

About the Program

While tools like ChatGPT allow the public to use AI for various tasks, computer scientists around the world are hard at work applying AI to some of the most critical problems in society. CS&E researchers are applying AI techniques to combat problems in the healthcare space - like clinician burnout, disease prediction, and data imbalance issues in biomedical data science.

Learn more about our AI efforts at z.umn.edu/AIforchange 
Check out our medical AI initiatives at z.umn.edu/MedicalAIPrograms