Past events

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to present their work to an audience of the Institute for Mathematics and Its Applications (IMA) postdocs, visitors, and graduate students, offering a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25-2:25 p.m.

Registration is required to access the Zoom webinar.

This week, Dan Kushnir (Nokia Bell Labs), will be giving a talk titled "Active Community Detection with Maximal Expected Model Change".

View the full list of IMA Industrial Problems seminars.

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Levon Nurbekyan (University of California, Los Angeles), will be giving a talk titled "Machine Learning Methods for Solving High-dimensional Mean-field Game Systems".

Registration is required to access the Zoom webinar.

Abstract

Mean-field games (MFG) is a framework to model and analyze huge populations of interacting agents that play non-cooperative differential games with applications in crowd motion, economics, finance, etc. Additionally, the PDE that arise in MFG have a rich mathematical structure and include those that appear in optimal transportation and density flow problems. In this talk, I will discuss applications of machine-learning techniques to solve high-dimensional MFG systems. I will present Lagrangian, GAN-type, and kernel-based methods for suitable types of MFG systems.

View the full list of IMA data science seminars.

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to present their work to an audience of the Institute for Mathematics and Its Applications (IMA) postdocs, visitors, and graduate students, offering a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25-2:25 p.m.

Registration is required to access the Zoom webinar.

This week, Tom Bliss (National Football League (NFL)), will be giving a talk titled "Estimating the Impact of Travel, Rest, and Playing at Home in the National Football League".

View the full list of IMA Industrial Problems seminars.

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 Wednesday from 3:30 p.m. - 4:30 p.m. during the Fall 2020 semester.

This week's speaker, Sandra Safo (University of Minnesota Division of Biostatistics) will be giving a talk about titled "Joint association and classification of multi-view structured data".

Abstract

Biography

Sandra Safo is an Assistant Professor of school of Public Health at University of Minnesota. Her primary research focuses on developing and applying statistical and machine learning methods and computational tools for big, biomedical data to advance clinical translational research and precision medicine. I have been developing multivariate statistical methods, statistical learning (including classification, discriminant analysis, association studies, biclustering), data integration, and feature selection methods for high dimensional data. Currently, I develop methods for integrative analysis of “omics” (including genomics, transcriptomics, and metabolomics) and clinical data to help elucidate complex interactions of these multifaceted data types.

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Anna Little (The University of Utah), will be giving a talk titled "Clustering High-dimensional Data with Path Metrics: A Balance of Density and Geometry".

Registration is required to access the Zoom webinar.

Abstract

This talk discusses multiple methods for clustering high-dimensional data, and explores the delicate balance between utilizing data density and data geometry. I will first present path-based spectral clustering, a novel approach which combines a density-based metric with graph-based clustering. This density-based path metric allows for fast algorithms and strong theoretical guarantees when clusters concentrate around low-dimensional sets. However, the method suffers from a loss of geometric information, information which is preserved by simple linear dimension reduction methods such as classic multidimensional scaling (CMDS). The second part of the talk will explore when CMDS followed by a simple clustering algorithm can exactly recover all cluster labels with high probability. However, scaling conditions become increasingly restrictive as the ambient dimension increases, and the method will fail for irregularly shaped clusters. Finally, I will discuss how a more general family of path metrics, combined with MDS, give low-dimensional embeddings which respect both data density and data geometry. This new method exhibits promising performance on single cell RNA sequence data and can be computed efficiently by restriction to a sparse graph.

View the full list of IMA data science seminars.

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 Wednesday from 3:30 p.m. - 4:30 p.m. during the Fall 2020 semester.

This week's speaker, Xiaoou Li (University of Minnesota School of Statistics) will be giving a talk about sequential analysis and adaptive design, latent variable models, and graphical models.

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Michael Osterholm (University of Minnesota), will be giving a talk titled "How COVID-19 has Changed the World and What the Future Holds".

Registration is required to access the Zoom webinar.

Abstract

This presentation will provide a current and in depth review of the COVID-19 pandemic. It will also provide a glimpse into the future as to how this pandemic will continue to unfold and the impact it will have worldwide.

View the full list of IMA data science seminars.

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Tyler Maunu (Massachusetts Institute of Technology), will be giving the lecture.

View the full list of IMA data science seminars.

Are you a student with an interest in data science? The University of Minnesota is hosting a new program in Data Science in Multi-Messenger Astrophysics, geared to prepare graduate (M.S. and Ph.D) students for careers in data science, in both academia and industry.

In addition to providing research opportunities at the frontier of astrophysics, this program also includes opportunities for developing professional skills, internships, outreach activities, and others.

If you would like to learn more about this program, please sign up for one of two information sessions:

• Tuesday, October 13 at 12:30 p.m. Central
• Tuesday, November 10 at 5:00 p.m. Central

Join us to hear about the multitude of career opportunities that await you!

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Daniel F. Keefe (University of Minnesota; a member of the data science faculty), will be giving a talk titled "Humanizing Data with Interactive Visualization".

Abstract

Data-intensive computing is central to so many aspects of society today.  Scientists and engineers continue to collect and simulate data that challenge our most sophisticated computational tools.  However, today's users of data-intensive computing extend well beyond these "traditional users" to include, for example, designers, visual artists, the general public, and Indigenous communities.  Our research explores how processes of analyzing and communicating about data will change in the future and can better serve this wide range of users and computing applications.  Our methods, employed with interdisciplinary collaborators across a range of projects, include a combination of novel visual designs, interactive techniques, and computer graphics and data processing algorithms.  In this talk, I will present specific examples that include: 1) advanced art-inspired algorithms for rendering multi-variate global climate data in immersive environments, 2) interactive simulation-based engineering design tools for understanding supercomputer ensemble datasets, and 3) interdisciplinary cultural revitalization and data storytelling within the UMN Indigenous Futures Grand Challenges project.

Biography

Dan Keefe is a Distinguished University Teaching Professor and Associate Professor in the Department of Computer Science and Engineering at the University of Minnesota. His research centers on interactive data visualization, immersive computer graphics, art+science collaborations, and computing for social good. Keefe’s awards include the National Science Foundation CAREER award; the University of Minnesota Guillermo E. Borja Award for research and scholarly accomplishments at the time of tenure; the University of Minnesota McKnight Land-Grant Professorship; and the 3M Non-tenured Faculty Award. He also shares multiple IEEE and ACM conference best paper awards with his students and collaborators.  Outside of computer science venues, Keefe has published and exhibited work in top international venues for digital art, such as South by Southwest, Northern Spark, ISEA, and Leonardo.  His research and art practice have been supported by grants from the National Science Foundation; the National Institutes of Health; the National Academies Keck Futures Initiative; the US Forest Service; the City of Minneapolis office of Arts, Culture, and the Creative Economy; and industry. Before joining the University of Minnesota, Keefe did post-doctoral work at Brown University jointly with the departments of Computer Science and Ecology and Evolutionary Biology and with the Rhode Island School of Design. He received the Ph.D. in 2007 from Brown University’s Department of Computer Science and the B.S. in Computer Engineering summa cum laude from Tufts University in 1999.