Data Science Seminars
The 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.
Some of the talks will be by group members of the IMA Data Science Lab discussing recent developments in the area and possibly presenting their own work, while other talks will be by local and visiting data scientists with a few talks by external speakers.
The seminars are organized by Jeff Calder, Will Leeb, and Gilad Lerman of the School of Mathematics at the University of Minnesota, and will generally take place Tuesdays from 1:25-2:25 p.m. in Walter Library 402 for or by Zoom. Please check the individual seminar page for details.
2021-2022
May 3, 2022
Free Boundary Problems on Lattices
Charles Smart (Yale University)
April 26, 2022
A Distributed Linear Solver via the Kaczmarz Algorithm
Eric Weber (Iowa State University)
April 19, 2022
A Characteristics-based Approach to Computing Tukey Depths
Martin Molina-Fructuoso (North Carolina State University)
April 12, 2022
How Well Can We Generalize Nonlinear Learning Models in High Dimensions??
Inbar Seroussi (Weizmann Institute of Science)
April 5, 2022
Method of Moments: From Sample Complexity to Efficient Implicit Computations
Joao Pereira (The University of Texas at Austin)
March 29, 2022
Relaxing Gaussian Assumptions in High Dimensional Statistical Procedures
Larry Goldstein (University of Southern California)
March 22, 2022
Using Artificial Intelligence to Model and Support the Management of Multimorbid Patients
Martin Michalowski (University of Minnesota, Twin Cities)
March 15, 2022
Auto-differentiable Ensemble Kalman Filters
Daniel Sanz-Alonso (University of Chicago)
March 1, 2022
On Multiclass Adversarial Training, Perimeter Minimization, and Multimarginal Optimal Transport Problems
Nicolas Garcia Trillos (University of Wisconsin, Madison)
February 22, 2022
Integrative Discriminant Analysis Methods for Multi-view Data
Sandra Safo (University of Minnesota, Twin Cities)
February 15, 2022
Graph Clustering Dynamics: From Spectral to Mean Shift
Katy Craig (University of California, Santa Barbara)
February 8, 2022
Decomposing Low-Rank Symmetric Tensors
Joe Kileel (The University of Texas at Austin)
February 1, 2022
Stability and Generalization in Graph Convolutional Neural Networks
Ron Levie (Ludwig-Maximilians-Universität München)
January 25, 2022
Intelligent Randomized Algorithms for the Low CP-Rank Tensor Approximation Problem
Alex Gittens (Rensselaer Polytechnic Institute)
December 14, 2021
New Methods for Disease Prediction using Imaging and Genomics
Eran Halperin (UnitedHealth Group)
November 23, 2021
The Scattering Transform for Texture Synthesis and Molecular Generation
Michael Perlmutter (University of California, Los Angeles)
November 9, 2021
Non-Parametric Estimation of Manifolds from Noisy Data
Yariv Aizenbud (Yale University)
October 19, 2021
Data depths meet Hamilton-Jacobi equations
Ryan Murray (North Carolina State University)
October 12, 2021
Organizational Collaboration with Assisted Learning
Jie Ding (University of Minnesota, Twin Cities)
October 5, 2021
Scalable and Sample-Efficient Active Learning for Graph-Based Classification
Kevin Miller (University of California, Los Angeles)
September 28, 2021
Standardizing the Spectra of Count Data Matrices by Diagonal Scaling
Boris Landa (Yale University)
September 21, 2021
Handling model uncertainties via informative Goodness-of-Fit
Sara Algeri (University of Minnesota, Twin Cities)
September 14, 2021
PDE-inspired Methods for Graph-based Semi-supervised Learning
Jeff Calder (University of Minnesota, Twin Cities)a, Twin Cities)