Past events

RSVP Link
Thursday, May 15th, 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. 

College/University Commencement

2025 College of Science & Engineering Undergraduate Commencement

Thursday, May 15, 2025
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: [email protected]
Event website

2025 College of Liberal Arts Undergraduate Commencement

Sunday, May 18, 2025
3M Arena at Mariucci

Two ceremonies (according to CLA major): 11 am and 4 pm. Details will be released early in the Spring 2024 semester (check the CLA event page below for updates).

Questions: [email protected]
Event website
CLA Commencement FAQ Page

Student Speakers

Nhi Dang

Nhi is an international student from Vietnam majoring in computer science. She is a Global Gopher Student Leader and a CSE Alumni Board Student Rep. Passionate about teaching, she is in her 8th semester as a CS&E teaching assistant, and also established and runs the CSCI Tutor Room. In recognition of her contributions, she received the 2023 CSE DEI Leadership Showcase Award. After graduation, Nhi will pursue applied mathematics research at MIT.

Prashant Reddy Pilla

Published pre-print author on forecasting the S&P 500 with LSTM neural networks, Outreach Coordinator for the UMN Blockchain Club, Student Ambassador for both the College of Liberal Arts (previously) and College of Science & Engineering(current). As one of the youngest internationally certified yoga instructors at age 17, I blend a passion for decentralised finance (DeFi) innovation with mindfulness practices, championing both technological advancement and holistic well-being in every endeavor.

Event Details

Jeff Dean - Chief Scientist, Google Research and Google DeepMind
Title: Modern Advances in Machine Learning and What Will They Enable?

Wednesday, May 14
10:30 a.m. - 12 p.m.
3-180 Keller Hall

Schedule

10:30 - 11 a.m. | Pre-lecture reception (lobby outside of 3-180)
11 a.m. - 12 p.m. | Seminar (3-180)

Abstract

In this talk I'll highlight some of the research and computer systems advances that have come together to create the capabilities of modern AI models.  I'll discuss the Gemini family of models and discuss the research advances that these models are built on, and highlight some of the uses enabled by aspects like multimodality, long-context and in-context learning, and inference time computation to enable more sophisticated reasoning.  With powerful capabilities and continuing rapid advances, modern AI has the potential to reshape much of what we do.  I'll discuss some potential areas that may be radically shaped by AI developments, and how collaboration between AI researchers and practitioners, policymakers, and other stakeholders can maximize the upsides of AI and minimize its downsides.

This talk will present work done by many people at Google.

Biography

Jeff Dean joined Google in 1999 where he now serves as Google’s Chief Scientist, focusing on AI advances for Google DeepMind and Google Research. He is a co-lead of the Gemini project, and his areas of focus include machine learning and AI, and applications of AI to problems that help billions of people in societally beneficial ways. His work has been integral to many generations of Google’s search engine, its initial ad serving system, distributed computing infrastructure such as BigTable and MapReduce, Google's TPU machine learning hardware, the Tensorflow open-source machine learning system, and Gemini multimodal models, among many other things.

Jeff received a Ph.D. in Computer Science from the University of Washington and a B.S. in Computer Science & Economics from the University of Minnesota, summa cum laude.  He is a member of the U.S. National Academy of Engineering and of the American Academy of Arts and Sciences, and a Fellow of the Association for Computing Machinery (ACM) , and a winner of the 2012 ACM Prize in Computing and the 2021 IEEE John von Neumann medal.

RSVP Link
Thursday, May 8, 9 - 11 a.m.
University Recreation and Wellness Center - Beacon Room

All graduating students from the Computer Science, Data Science, Bioinformatics and Computational Biology, and Software Engineering 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.

College/University Commencement

Master's and Doctoral Degree Student Commencement 
Thursday, May 8 starting at 1 p.m.
3M Arena at Mariucci

Questions: [email protected]
Event website

Student Speakers

Rimika Dhara

Rimika Dhara grew up in Mumbai, India and moved to Rosemount, Minnesota when she was 14. She completed both her undergraduate and graduate degrees here at the University of Minnesota. Throughout her academic career, Dhara's focus has been computer science with a specialization in machine learning during her master's program. During her time at UMN, she was actively involved in several organizations. She worked as a researcher with the MinnesotaNLP research group and served in multiple leadership roles with the Science and Engineering Student Board, including Mental Health Director, Academic Affairs Director, and Student Outreach Director. She was also a member of MN Junoon, a Bollywood Fusion dance team, and participated in the Indian Student Association on campus. Outside of academics, Dhara enjoys dancing, painting, reading, and creative writing. Following graduation, she will be joining Oracle as a Software Engineering in January and continue to complete her thesis with the Minnesota NLP research group in the Fall.

Xiang Zhang

Xiang Zhang is a PhD graduate from Shenzhen in the Guangdong Province of China. They have a BS in computer science and engineering from The Chinese University of Hong Kong, Shenzhen, and an MS in electrical and computer engineering at UMN. Zhang's research focuses on computational biology and they hope to explore opportunities in the biotech/pharmaceutical industry to advance biomedical AI research. During their time at the University of Minnesota, Zhang was a computer science student ambassador of the Data Science Initiative with the College of Science and Engineering, as well as a member of the UMN Campus Orchestra and the UMN Traditional Karate-do Academy. They also volunteered for a number of department initiatives, including CS&E faculty interviews. Outside of the world of academia, Zhang enjoys skiing and going to Minnesota Orchestra concerts. 

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Xinyuan Zheng (University of Minnesota), will be giving a talk titled "Earth and Environmental Sciences in the Era of Big Data and AI".

Abstract

The Earth system is complex, dynamic, and interconnected across space and time. Understanding key processes such as ocean circulation, climate change, and environmental evolution increasingly depends on integrating large, diverse datasets—from geochemical measurements to satellite observations and model simulations. Many of these questions represent grand challenges facing human society today, with practical implications for environmental sustainability, economic resilience, and resource management. At the same time, Earth and environmental sciences address some of the most profound basic science questions, including the origin of life, the long-term evolution of Earth's environments, and the search for life beyond our planet. As the field moves into the era of big data and AI, new collaborations across disciplines—esp cially with computer science and engineering—are becoming essential for unlocking solutions and discoveries critical to both our future and our understanding of the universe. In this talk, I will highlight several case studies where such interdisciplinary collaborations are emerging or urgently needed.

Biography

Dr. Xinyuan Zheng is an Assistant Professor in the Department of Earth and Environmental Sciences at the University of Minnesota–Twin Cities. He received his PhD from University of Oxford. His research focuses on applying advanced isotope analysis and geochemical techniques to study Earth’s surface processes, ocean chemistry, and global environmental change. He is also interested in emerging data-driven approaches to tackle geological and environmental questions. Dr. Zheng is working on several interdisciplinary projects funded by the National Science Foundation, the American Chemical Society, and the National Institutes of Health. He received the NSF CAREER Award in 2023.

View Poster Details

The Department of Computer Science & Engineering is co-sponsoring an event with the Department of English. Essayist and critic Meghan O'Gieblyn, will speak about her book, God Human Animal Machine: Technology, Metaphor, and the Search for Meaning (Vintage).

This event is free and open to the public. For accessibility services and questions about the venue, contact Terri Sutton at [email protected] or 612-626-1528. 

Learn more at the Department of English event page.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Ken Goldberg (University of California, Berkeley), will be giving a talk titled "Is Data All You Need?: Large Robot Action Models and Good Old Fashioned Engineering".

Abstract

Enthusiasm has been skyrocketing for humanoids based on recent advances in "end-to-end" large robot action models. Initial results are promising, and several collaborative efforts are underway to collect the needed demonstration data. But is data really all you need?

Although end-to-end Large Vision, Language, Action (VLA) Models have potential to generalize and reliably solve all problems in robotics, initial results have been mixed.  It seems likely that the size of the VLA state space and dearth of available demonstration data, combined with challenges in getting models to generalize beyond the training distribution and the inherent challenges in interpreting and debugging large models, will make it difficult for pure end-to-end systems to provide the kind of robot performance that investors expect in the near future. 

In this presentation, I share my concerns about current trends in robotics, including task definition, data collection, and experimental evaluation.  I propose that to reach expected performance levels, we will need "Good Old Fashioned Engineering (GOFE)" – modularity, algorithms, and metrics.   I'll present MANIP, a modular systems architecture that can integrate learning with well-established procedural algorithmic primitives such as Inverse Kinematics, Kalman Filters, RANSAC outlier rejection, PID modules, etc. I’ll show how we are using MANIP to improve performance on robot manipulation tasks such as grasping, cable untangling, surgical suturing, motion planning, and bagging, and propose open directions for research.

Biography

Ken Goldberg is William S. Floyd Distinguished Chair of Engineering at UC Berkeley and Chief Scientist of Ambi Robotics and Jacobi Robotics. Ken leads research in robotics and automation: grasping, manipulation, and learning for applications in warehouses, industry, homes, agriculture, and robot-assisted surgery.  He is Professor of IEOR with appointments in EECS and Art Practice.  Ken is Chair of the Berkeley AI Research (BAIR) Steering Committee (60 faculty) and is co-founder and Editor-in-Chief emeritus of the IEEE Transactions on Automation

Science and Engineering (T-ASE). He has published ten US patents, over 400 refereed papers, and presented over 600 invited lectures to academic and corporate audiences. http://goldberg.berkeley.edu

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Jian Pei (Duke University), will be giving a talk titled "Data Valuation in Machine Learning, AI, and Data (MAD) Systems".

Abstract

Biography

Dr. Jian Pei is the Arthur S. Pearse Distinguished Professor at Duke University, specializing in data science, big data, data mining, database systems, and applied machine learning. His research focuses on developing efficient data analysis techniques for data-intensive applications. A Fellow of the Royal Society of Canada, the Canadian Academy of Engineering, ACM, and IEEE, he has contributed algorithms adopted in industry and integrated into open-source software. He has also led the development of large-scale commercial systems. His work has been recognized with honors such as the ACM SIGKDD Innovation Award (2017) and Service Award (2015).

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Dustin Wright (University of Copenhagen), will be giving a talk titled "Socially Sustainable NLP".

Abstract

Natural language processing (NLP) technology is rapidly being adopted by the public. This comes with a dual issue: on the one hand, NLP system can help in achieving economic, environmental, and social sustainability, while on the other hand, the technology itself risks being unsustainable to use. In this talk, I will discuss my work on NLP for social sustainability and the social sustainability of NLP, focusing on two case studies representative of these aspects. With respect to NLP for social sustainability, I will discuss work on NLP for science communication, demonstrating how NLP systems can help us better understand how misinformation is presented and introduced in different media about science. For the social sustainability of NLP, I will present results on measuring political bias in large language models (LLMs) through the lens of tropes: recurrent and consistent arguments that LLMs generate in response to political propositions. Finally, I will discuss how social sustainability ties into holistic sustainability in NLP, and which avenues of research I find most exciting towards achieving this.

Biography

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Alexander Spangher (University of Southern California), will be giving a talk titled "Towards Planning in Creative Contexts".

Abstract

Recent modeling innovations that teach large language models (LLMs) how to plan — or break down and solve complex problems into multiple steps — have allowed LLMs to achieve impressive results in domains like mathematical problem-solving and coding. However, tasks in such domains are often characterized by large training datasets and well-defined rewards. Many human-centered tasks, especially creative tasks, occur in contexts where goals and rewards are not as clearly defined and datasets are limited: thus, we lack the means necessary to train models to plan in such settings. In this talk, I will outline a research agenda that can enable us to make progress. I will show three pillars: (1) observing plans: how long-range text modeling can help us make inferences about past human actions based on state-observations (a process known to cognitive psychologists as "emulation, based on end-state observation"); (2) improving plans: how these inferences can help us benchmark LLMs in creative tasks and how hierarchical modeling can help us learn novel planning strategies; and (3) executing plans: how classifier-free guidance, an inference-time technique, can be utilized to help LLMs adhere to complex plans. I will demonstrate these processes in the domain of journalism, with specific focus on the task of helping journalists find sources to support their writing processes.

Biography