Past events

2022 CSE Undergraduate Commencement

Visit CSE Undergraduate Commencement Ceremony for more information!

Department of Computer Science & Engineering Undergraduate Student Graduation Event

RSVP Link

In addition to celebrating you at the 2022 commencements in CLA and CSE, our department will be hosting a department-specific graduation event to celebrate our graduating students, and you are invited! This event will be held on Thursday, May 12th, from 9:00 am -  11:00 am (remarks from the department will begin at 10:00) at the University of Minnesota Recreation and Wellness Center in the Beacon Room on the second floor.

Please RSVP for this event if you graduated recently, or you will graduate soon. Even if you are not able or planning to attend the event, you are still welcome to submit the RSVP form as a graduating student.

Caps and gowns are optional for our departmental event. However, we recommend dressing professionally if you plan on attending without a cap and gown. Masking is strongly recommended for the event but is no longer required. Light food and refreshments will be provided.

Other helpful links:

CLA Commencement Information

CSE Commencement Information

UMN Bookstore Graduation Link

International Student: Inviting Family

End of spring semester

The last day of the spring 2022 semester is Wednesday, May 11.

View the full academic schedule on One Stop.
 

Minnesota Natural Language Processing Seminar Series: Yoon Kim

The Minnesota Natural Language Processing (NLP) Seminar is a venue for faculty, postdocs, students, and anyone else interested in theoretical, computational, and human-centric aspects of natural language processing to exchange ideas and foster collaboration. The talks are every other Friday from 12 p.m. - 1 p.m. during the Spring 2022 semester.

This week's speaker, Yoon Kim (MIT), will be giving a talk titled "Efficient Transfer Learning with Large Language Models"

Abstract 

Transfer learning with large pretrained language models is the dominant paradigm in natural language processing. With moderately-sized models (e.g., BERT), transfer learning involves full finetuning to obtain a task-specific model with its own parameters for each task, which makes the approach hard to scale to storage-constrained scenarios. With larger models (e.g., GPT-3), the model is adapted to each task via natural language prompts and thus the pretrained parameters remain fixed. However, few-shot learning capabilities via prompting emerge only when model sizes are large enough, and thus inference remains expensive. This talk explores two approaches for improving the memory- and inference-efficiency of large language models within the transfer learning paradigm. For finetuned models, we show that only a small subset of the model parameters (0.5%) need to be updated to match the performance of fully-finetuned models. For prompted models, we show that co-training (wherein two models are trained on confidently-labeled outputs from each other) can produce much smaller models that outperform the original prompted model.

Biography

Yoon Kim is an assistant professor at MIT in the Department of Electrical Engineering and Computer Science. He obtained his PhD from Harvard University, where he was advised by Alexander Rush.

Final exams begin

Final exams for spring 2022 will be held between Thursday, May 5 and Wednesday, May 11.

Monday, May 3 and Tuesday, May 4 are study days.

View the full academic schedule on One Stop.
 

Last day of instruction

The last day of instruction for the spring 2022 semester is Monday, May 2.

View the full academic schedule on One Stop.
 

Department of Computer Science & Engineering Graduate Student Graduation Event

RSVP Link

Our Graduate Student Graduation Celebration event will be held Friday, April 29th from 9:00 - 11:00 (remarks from the department at 10:00 am) held in the University of Minnesota Recreation and Wellness Center Beacon Room on the second floor. Please RSVP as soon as possible. You’re invited if you’re a graduate of our Computer Science, Data Science, or Bioinformatics and Computational Biology graduate program between Summer 2021 through Fall 2022. 

Caps and gowns are optional for our departmental event. We recommend dressing nicely if you plan on attending without a cap and gown. Masking is strongly recommended for the event but is no longer required. Light food and refreshments will be provided.

Note this is not the commencement ceremony this is a social gathering for graduates of CS&E programs only. The commencement ceremony will be at 12 pm in Mariucci the same day.

ASE Graduate Commencement Ceremony

ASE Graduate Commencement for 2022 is also Friday, April 29th starting at noon located at 3M Arena at Mariucci. Information detailing the Graduate Commencement event

Other helpful links:

UMN Bookstore Graduation Link

International Student: Inviting Family

Minnesota Natural Language Processing Seminar Series: Investigating Language in the Brain Using Artificial Neural Networks

The Minnesota Natural Language Processing (NLP) Seminar is a venue for faculty, postdocs, students, and anyone else interested in theoretical, computational, and human-centric aspects of natural language processing to exchange ideas and foster collaboration. The talks are every other Friday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Greta Tuckute (MIT), will be giving a talk titled "Investigating language in the brain using artificial neural networks."

Abstract

The human language system allows us to infer meaning from text and speech. The unique human ability to comprehend language depends on a left-lateralized fronto-temporal brain network that responds robustly and selectively to linguistic input. A big open question in cognitive science and neuroscience concerns the organization of the language system. Until recently, we had no computationally precise models that could serve as quantitative hypotheses for how core aspects of language might be implemented in the mind and brain. However, artificial neural networks (ANNs) for language have suddenly achieved impressive performance on a wide range of language tasks – prompting the question of whether ANNs can serve as the first computationally precise models of how the human brain may solve the same tasks. 
In my talk, I will discuss: i) ANNs as models of sensory systems, ii) Methodological approaches and assumptions underlying the use of ANNs as models of language processing, iii) Findings from a large-scale investigation1 of 43 diverse ANN language models as models of human neural (fMRI/ECoG) and behavioral responses. In brief, we found that match-to-brain correlated with next-word prediction performance of ANNs (but not performance on other GLUE benchmarks) and we thus claim that a drive to predict future inputs may shape human language processing.

Schrimpf, M., Blank, I.A., Tuckute, G., Kauf, C., Hosseini, E.A., Kanwisher, N.G., Tenenbaum, J.B., & Fedorenko, E. (2021). The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences, 118.

Biography

Greta Tuckute is a PhD student in Brain and Cognitive sciences at MIT working with Dr. Ev Fedorenko. She obtained her BSc and MSc degrees from University of Copenhagen in Denmark. She is now working in the intersection of neuroscience and AI, and is interested in exploiting artificial neural networks to understand how language is processed in the mind and brain.

Minnesota Natural Language Processing Seminar Series: Reliable and Factual Natural Language Generation

The Minnesota Natural Language Processing (NLP) Seminar is a venue for faculty, postdocs, students, and anyone else interested in theoretical, computational, and human-centric aspects of natural language processing to exchange ideas and foster collaboration. The talks are every other Friday from 12 p.m. - 1 p.m. during the Spring 2022 semester.

This week's speaker, He He (NYU), will be giving a talk titled "Reliable and Factual Natural Language Generation"

Abstract 

Recent advances in large-scale neural language models have transformed the field of text generation, including applications like dialogue and document summarization. Despite human-like fluency, the generated text tends to contain incorrect, inconsistent, or hallucinated information, which hinders the deployment of text generation models in real applications. I will review observations of such errors in current generation tasks, explain challenges in evaluating and mitigating factual errors, and describe our recent attempts on addressing these problems. I will conclude with a discussion on future challenges and directions.

Biography

He He is an assistant professor at the Center for Data Science and the Department of Computer Science at New York University. Before joining NYU, she spent a year at Amazon Web Services and was a postdoc at Stanford University. She received her PhD from University of Maryland, College Park. She is interested in building trustworthy NLP systems in human-centered applications. Her current research focuses on text generation, dialogue systems, and robust language understanding.

Human-Centered Computing Webinar

Join fellow alumni and friends to learn how University of Minnesota researchers are advancing the theory and practice of human-centered and social computing by experimenting with systems that impact the lives of real people. During this virtual panel, Computer Science & Engineering faculty from the GroupLens lab will give an overview of current research directions that they are most excited about, including:
- Using Human-Centered Machine Learning to identify dangerous health behaviors in online communities
- Understanding the nature of social support in online health communities, and creating techniques to enable more and better support
- Creating novel systems for supporting children’s social connections in family and school contexts
- Personalization in recommender systems; designing recommender systems to serve the needs of multiple stakeholders
- How bulk email is flooding organizations, and what to do about it
- How to make video conference meetings more effective and equitable.

Following a brief presentation on their thoughts on these topics, the panelists will open the floor for general Q&A.