Upcoming events

AI for Mental Health Webinar with Stevie Chancellor

Artificial Intelligence is now at the forefront of technological interest in solving socially challenging problems, like identifying people who may discuss dangerous mental illness behaviors online (e.g. suicidal ideation, self-injury, and opioid addiction). We urgently need better AI to handle the volume and risk of this content in social networks. However, it is important to create AI and interventions in this space that are ethical and helpful, or we risk harming the very people we intend to help. Assistant Professor Stevie Chancellor will discuss the promises and perils for AI that predicts and intervenes in mental health in social media, and what we can do to make it more technically rigorous, ethical, and compassionate toward people in distress. She will discuss recent work in her research group on TikTok and mental health, and visions for the future of AI to assist in socially hard problems.

 
 

MSSE Information Session (In Person)

Learn about the MSSE program at the upcoming in-person information session on February 28 from 1-2 p.m. CST.

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 to attend!

CS&E Colloquium: Fundamental Problems in AI: Transferability, Compressibility and Generalization

The computer science colloquium takes place on Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Tomer Galanti (MIT), will be giving a talk titled "Fundamental Problems in AI: Transferability, Compressibility and Generalization".

Abstract

In this talk, we delve into several fundamental questions in deep learning. We start by addressing the question, "What are good representations of data?" Recent studies have shown that the representations learned by a single classifier over multiple classes can be easily adapted to new classes with very few samples. We offer a compelling explanation for this behavior by drawing a relationship between transferability and an emergent property known as neural collapse. Later, we explore why certain architectures, such as convolutional networks, outperform fully-connected networks, providing theoretical support for how their inherent sparsity aids learning with fewer samples. Lastly, I present recent findings on how training hyperparameters implicitly control the ranks of weight matrices, consequently affecting the model's compressibility and the dimensionality of the learned features.

Additionally, I will describe how this research integrates into a broader research program where I aim to develop realistic models of contemporary learning settings to guide practices in deep learning and artificial intelligence. Utilizing both theory and experiments, I study fundamental questions in the field of deep learning, including why certain architectural choices improve performance or convergence rates, when transfer learning and self-supervised learning work, and what kinds of data representations are learned in practical settings.

Biography

Tomer Galanti is a Postdoctoral Associate at the Center for Brains, Minds, and Machines at MIT, where he focuses on the theoretical and algorithmic aspects of deep learning. He received his Ph.D. in Computer Science from Tel Aviv University, during which he served as a Research Scientist Intern at Google DeepMind's Foundations team. He has published numerous papers in top-tier conferences and journals, including NeurIPS, ICML, ICLR, and JMLR. Notably, his paper "On the Modularity of Hypernetworks" was awarded an oral presentation at NeurIPS 2020.

Computer Science M.S. application deadline for fall

Computer Science M.S. application deadline for fall. Applications must be submitted by 11:59 p.m. CST.

We offer fall admission only and do not admit for the spring semester.

Computer Science M.C.S. application deadline for fall

Computer Science M.C.S. application deadline for fall. Applications must be submitted by 11:59 p.m. CST.

We offer fall admission only and do not admit for the spring semester.

Data Science M.S. application deadline for fall

Data Science applications must be completed and submitted by or before 11:59 p.m. CST to be reviewed. This deadline applies to all domestic and international applicants.

We only offer fall admission for the M.S. program. We do not offer spring admission for the M.S. program.



 

Post-Baccalaureate Certificate application deadline for fall

Post-Baccalaureate Certificate application deadline for fall admission.

Admission to the Post-Baccalaureate Certificate is open for both fall and spring.



 

CS&E Colloquium: Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design

The computer science colloquium takes place on Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Aryan Deshwal (Washington State University), will be giving a talk titled "Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design".

Abstract

A huge range of scientific discovery and engineering design problems ranging from materials discovery and drug design to 3D printing and chip design can be formulated as the following general problem: adaptive optimization of complex design spaces guided by expensive experiments where expense is measured in terms of resources consumed by the experiments. For example, searching the space of materials for a desired property while minimizing the total resource-cost of physical lab experiments for their evaluation. The key challenge is how to select the sequence of experiments to uncover high-quality solutions for a given resource budget.

In this talk, I will introduce novel adaptive experiment design algorithms to optimize combinatorial spaces (e.g., sequences and graphs). First, I will present a dictionary-based surrogate model for high-dimensional fixed-size structures. Second, I will discuss a surrogate modeling approach for varying-size structures by synergistically combining the strengths of deep generative models and domain knowledge in the form of expert-designed kernels. Third, I will describe a general output space entropy search framework to select experiments for the challenging real-world scenario of optimizing multiple conflicting objectives using multi-fidelity experiments that trade-off resource cost and accuracy of evaluation. I will also present results on applying these algorithms to solve high-impact science and engineering applications in domains including nanoporous materials discovery, electronic design automation, additive manufacturing, and optimizing commercial Intel systems.

Biography

Aryan Deshwal is a final year PhD candidate in CS at Washington State University. His research agenda is AI to Accelerate Scientific Discovery and Engineering Design where he focuses on advancing foundations of AI/ML to solve challenging real-world problems with high societal impact in collaboration with domain experts. He is selected for Rising Stars in AI by KAUST AI Initiative (2023) and Heidelberg Laureate Forum (2022). He won the College of Engineering Outstanding Dissertation Award (2023), Outstanding Research Assistant Award (2022), and Outstanding Teaching Assistant in CS Award (2020) from WSU. He won outstanding reviewer awards from ICML (2020), ICLR (2021), and ICML (2021) conferences.

ML Seminar: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

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
     

RSVP for the next information session now