Past events

Rooted in STEM Information Session

Are you a current CSE undergraduate student interested in doing high school outreach to local high school 11th/12th graders? Please consider the brand new Rooted in STEM program!

Information Session for Potential Mentors
Friday, September 17
4 - 5 p.m.
Bruininks Hall 420B

About the Program
This program will focus on historically excluded and underserved students from Twin Cities high schools who are interested in STEM and will be capped at 25 participants. The ratio for mentors/mentees will likely be one undergraduate mentor to every three high school mentees.

Undergraduates with BIPOC, First Generation, and LGBTQIA+ identities are especially encouraged to apply!

Requirements for Mentors
Mentors will be asked to mentor one Saturday per month (October-April) for 4 hours each in addition to several pre-program trainings.

To support 25 high school participants, Rooted in STEM is now recruiting 8-9 CSE undergraduate students with a 2.5+ cumulative GPA to serve as mentors. Highly dedicated mentors will be essential program staff and must be able to fulfil to the following:

Minimum Commitment

  • Pass a background check required by the University’s Safety of Minors policy (For more information, please visit the policy website: policy.umn.edu/operations/minorsafety)
  • Attend required mentor training on EITHER Friday, October 8 from 8:00-10:00am OR Wednesday, October 13 from 11:30am-1:00pm.
  • Attend all program sessions from 9:30am-1:30pm on the following Saturdays: October 16, November 13, December 11, January 22, February 19, March 19, and April 30
  • Attend periodic mentor meetings


Perks

  • Mentors who fulfill their commitment will be eligible for a $500 scholarship at the end of the program
  • Catered lunch provided during Saturday program sessions


Questions? Email Dan Garrison, Assistant Director for Diversity and Inclusion for CSE Collegiate Life.

 

Industrial Problems Seminar: SIAM Internship Panel

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week will be a SIAM Internship Panel.

Come learn about the process of finding, interviewing, and getting jobs in industry! Panelists Brendan Cook, Jacob Hegna, Drisana Mosaphir, Cole Wyeth, and Amber Yuan will be here to answer all your questions about finding and participating in internships both before and during the pandemic.

UMN Machine Learning Seminar: Tackling the Challenges of Next-generation Healthcare: NVIDIA’s Applied Research in Medical Imaging

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 Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Holger Roth (NVIDIA), will be giving a talk titled "Tackling the Challenges of Next-generation Healthcare: NVIDIA’s Applied Research in Medical Imaging."

Abstract

Recent advances in computer vision and artificial intelligence caused a paradigm shift in medical image computing and radiological image analysis. Deep learning has been widely applied to many radiological applications, replacing, or working together with conventional methods. The advantage of being able to learn from that data directly is promising for many imaging tasks. Some key factors and current challenges preventing the widespread adaption of machine learning techniques in the clinic are algorithmic considerations, computational power, and, most critically, high-quality data for training.

NVIDIA wants to provide solutions to make the widespread adoption of deep learning and artificial intelligence easier in the real world. This talk will highlight NVIDIA’s efforts in the healthcare sector and medical imaging research, for example, around federated learning and COVID-19 image analysis, and introduce platforms & hardware considerations for modern machine learning at scale.

Biography

Holger Roth is a Sr. Applied Research Scientist at NVIDIA focusing on deep learning for medical imaging. He has been working closely with clinicians and academics over the past several years to develop deep learning based medical image computing and computer-aided detection models for radiological applications. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award.

Last day to receive a 100% tuition refund for canceling full semester classes

The last day to receive a 100% tuition refund for canceling full semester classes is Tuesday, September 14.

View the full academic schedule on One Stop.
 

IMA Data Science Seminar

The Institute for Mathematics and Its Applications (IMA) Data Science Seminars are a forum for data scientists of IMA academic and industrial partners to discuss and learn about recent developments in the broad area of data science. The seminars take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week's speaker is Danny Abrams (Northwestern University).

Industrial Problems Seminar: Being Smart and Dumb - Building the Sports Analytics Industry

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Dean Oliver ( NBA's Washington Wizards), will be giving a talk titled "Being Smart and Dumb: Building the Sports Analytics Industry."

Registration is required to access the Zoom webinar.

Abstract

Going from a scientific background into something that people haven't done comes with moments where you don't know what you're talking about... if you talk, that is. Admitting the times you don't know how your work can help and introducing your work when it may be able to help - that timing can be hard. I went from the field I was trained in- environmental engineering and consulting - to a job with no title at first. I had to write a book about how stats can help in basketball. Someone else invented the term "Sports Analytics". This talk is a little bit of that story.

Biography

Lawrence Dean Oliver is an American statistician and assistant coach for the NBA's Washington Wizards. Oliver is a prominent contributor to the advanced statistical evaluation of basketball. He is the author of Basketball on Paper, the former producer of the defunct Journal of Basketball Studies.

UMN Machine Learning Seminar

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 Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker is Tuo Zhao (Georgia Tech).

Abstract

Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. When we only have limited supervision for the downstream tasks, however, due to the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data.

To address such a concern, we propose a new approach for fine-tuning of pretrained models to attain better generalization performance. Our proposed approach adopts three important ingredients: (1) Smoothness-inducing adversarial regularization, which effectively controls the complexity of the massive model; (2) Bregman proximal point optimization, which is an instance of trust-region algorithms and can prevent aggressive updating; (3) Differentiable programming, which can mitigate the undesired bias induced by conventional adversarial training algorithms. Our experiments show that the proposed approach significantly outperforms existing methods in multiple NLP tasks. In addition, our theoretical analysis provides some new insights of adversarial training for improving generalization.

Biography

Tuo Zhao is an assistant professor at Georgia Tech. He received his Ph.D. degree in Computer Science at Johns Hopkins University. His research mainly focuses on developing methodologies, algorithms and theories for machine learning, especially deep learning. He is also actively working in neural language models and open-source machine learning software for scientific data analysis. He has received several awards, including the winner of INDI ADHD-200 global competition, ASA best student paper award on statistical computing, INFORMS best paper award on data mining and Google faculty research award.

First day of classes

Welcome back! The fall 2021 semester begins on Tuesday, September 7.

View the full academic schedule on One Stop.
 

University closed

The University of Minnesota will be closed in observance of Labor Day.

View the full schedule of University holidays.
 

UMN Machine Learning Seminar

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 Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.

This week's speaker, Simon Batzner (Harvard University) will be giving a talk titled "Causal Inference from Slowly Varying Nonstationary Processes."

Abstract

Representations of atomistic systems for machine learning must transform predictably under the geometric transformations of 3D space, in particular rotation, translation, mirrors, and permutation of atoms of the same species. These constraints are typically satisfied by means of atomistic representations that depend on scalar distances and angles, leaving the representation invariant under the above transformations. Invariance, however, limits the expressivity and can lead to an incompleteness of representations. In order to overcome this shortcoming, we recently introduced Neural Equviariant Interatomic Potentials [1], a Graph Neural Network approach for learning interatomic potentials that uses a E(3)-equivariant representation of atomic environments. While most current Graph Neural Network interatomic potentials use invariant convolutions over scalar features, NequIP instead employs equivariant convolutions over geometric tensors (scalar, vectors, …), providing a more information-rich message passing scheme. In my talk, I will first motivate the choice of an equivariant representation for atomistic systems and demonstrate how it allows for the design of interatomic potentials at previously unattainable accuracy. I will discuss applications on a diverse set of molecules and materials, including small organic molecules, water in different phases, a catalytic surface reaction, proteins, glass formation of a lithium phosphate, and Li diffusion in a superionic conductor. I will then show that NequIP can predict structural and kinetic properties from molecular dynamics simulations in excellent agreement with ab-initio simulations. The talk will then discuss the observation of a remarkable sample efficiency in equivariant interatomic potentials which outperform existing neural network potentials with up to 1000x fewer training data and rival or even surpass the sample efficiency of kernel methods. Finally, I will discuss potential reasons for the high sample efficiency of equivariant interatomic potentials.

Biography

Batzner is a mathematician and machine learning researcher at Harvard. Previously, he worked on machine learning at MIT, wrote software on a NASA mission, and spent some time at McKinsey. He enjoys working with ambitious people who want to change the world.