April 06, 2020 
Enhancing T Cell Migration through Structurally and Mechanically Complex Tumor Microenvironments Paolo Provenzano (University of Minnesota, Twin Cities) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Lecture Alexander Anderson (Moffitt Cancer Center) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 06, 2020 
Model systems toward understanding metastasis and overcoming drug resistance of breast cancer Shelly Peyton (University of Massachusetts) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Lecture Edna Cukierman (Fox Chase Cancer Center) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 06, 2020 
Wrapup Discussion 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
TumorMatrix Interactions in Early Ductal Invasions: integrating histology, mechanobiology and computational modeling Katarzyna (Kasia) Rejniak (Moffitt Cancer Center) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Lecture Kaylee Schwertfeger (University of Minnesota, Twin Cities) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Developing experimental and mathematical models to measure changes in tumor associate macrophage polarization in response to immunotherapy Elizabeth Wayne (Carnegie Mellon University) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Microenvironmental barriers to immune effector cell infiltration and activity in PDA Rolf Brekken (UT Southwestern Medical Center) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Lecture Eugene Koay (University of Texas M. D. Anderson Cancer Center) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 07, 2020 
Wrapup Discussion 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
April 08, 2020 
A New Mouse Model Provides Insight Into the Ductal Cell Origin and Microenvironment of Human PDAC Daniel Billadeau (Mayo Clinic) 
CANCELED: Quantitative Analysis and Mathematical Modeling to Capture Complex Dynamics of the Tumor Microenvironment 
November 11, 2020 
Optimal Control of Fluid Transport Networks Anatoly Zlotnik (Los Alamos National Laboratory) 
Optimal Control, Optimal Transport, and Data Science 
November 11, 2020 
Active Learning and Optimal Experimental Design Eldad Haber (University of British Columbia) 
Optimal Control, Optimal Transport, and Data Science 
November 11, 2020 
A Fast Approach to Optimal Transport: The Backandforth Method Matthew Jacobs (University of California, Los Angeles) 
Optimal Control, Optimal Transport, and Data Science 
November 12, 2020 
Wasserstein Control of Mirror Langevin Monte Carlo Kelvin Shuangjian Zhang (École Normale Supérieure) 
Optimal Control, Optimal Transport, and Data Science 
November 12, 2020 
Stochastic Methods for Optimal Transport in Machine Learning Applications Aude Genevay (Massachusetts Institute of Technology) 
Optimal Control, Optimal Transport, and Data Science 
November 12, 2020 
Prediction with Expert Advice: A PDE Perspective on a Model Problem from Online Machine Learning Robert Kohn (New York University) 
Optimal Control, Optimal Transport, and Data Science 
November 12, 2020 
Information in Mean Field Control Aaron Palmer (University of British Columbia) 
Optimal Control, Optimal Transport, and Data Science 
November 13, 2020 
Gradient Flows in the Wasserstein Metric: From Discrete to Continuum via Regularization Katy Craig (University of California, Santa Barbara) 
Optimal Control, Optimal Transport, and Data Science 
November 09, 2020 
The Quadratic Wasserstein Metric for Inverse Data Matching Problems Yunan Yang (New York University) 
Optimal Control, Optimal Transport, and Data Science 
November 09, 2020 
Summarizing and Analyzing Data using Optimal Transport Justin Solomon (Massachusetts Institute of Technology) 
Optimal Control, Optimal Transport, and Data Science 
November 09, 2020 
Learned Adversarial Regularisers Carola Schoenlieb (University of Cambridge (Cambridge, GB)) 
Optimal Control, Optimal Transport, and Data Science 
November 09, 2020 
Graphical Optimal Transport Yongxin Chen (Georgia Institute of Technology) 
Optimal Control, Optimal Transport, and Data Science 
November 10, 2020 
On The Convergence of MMD GANs: A Theory via Parametric Kernelized Gradient Flows Youssef Mroueh (IBM) 
Optimal Control, Optimal Transport, and Data Science 
November 10, 2020 
Mathematical Approaches to Deep Learning: Model Uncertainty, Robustness and Regularization Adam Oberman (McGill University) 
Optimal Control, Optimal Transport, and Data Science 
November 11, 2020 
Streaming Computation of Optimal Weak Transport Barycenters Elsa Cazelles (Institut de Recherche en Informatique de Toulouse) 
Optimal Control, Optimal Transport, and Data Science 
November 10, 2020 
A PDE Interpretation of Prediction with Expert Advice Nadejda Drenska (University of Minnesota, Twin Cities) 
Optimal Control, Optimal Transport, and Data Science 
November 10, 2020 
Deep Learning and Optimal Control Weinan E (Princeton University) 
Optimal Control, Optimal Transport, and Data Science 
September 17, 2020 
Community Detection Using Total Variation and Surface Tension Zach Boyd (University of North Carolina, Chapel Hill) 
Theory and Algorithms in Graphbased Learning 
September 14, 2020 
A Fast GraphBased Data Classification Method with Applications to 3D Sensory Data in the Form of Point Clouds Ekaterina Rapinchuk (Michigan State University) 
Theory and Algorithms in Graphbased Learning 
September 15, 2020 
Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization Yifei Lou (University of Texas at Dallas) 
Theory and Algorithms in Graphbased Learning 
September 17, 2020 
On Wasserstein Gradient Flows and the Search of Neural Network Architectures Nicolas Garcia Trillos (University of Wisconsin, Madison) 
Theory and Algorithms in Graphbased Learning 
September 18, 2020 
Learning Restricted Boltzmann Machines Ankur Moitra (Massachusetts Institute of Technology) 
Theory and Algorithms in Graphbased Learning 
September 18, 2020 
Learning Discrete Graphical Models: Exact & Neural Network Assisted Methods Marc Vuffray (Los Alamos National Laboratory) 
Theory and Algorithms in Graphbased Learning 
September 14, 2020 
A Selfavoiding Approximate Mean Curvature Flow Simon Masnou (Université ClaudeBernard (Lyon I)) 
Theory and Algorithms in Graphbased Learning 
September 14, 2020 
Reducibility and StatisticalComputational Gaps from Secret Leakage Guy Bresler (Massachusetts Institute of Technology) 
Theory and Algorithms in Graphbased Learning 
September 14, 2020 
Robust Group Synchronization via CycleEdge Message Passing Gilad Lerman (University of Minnesota, Twin Cities) 
Theory and Algorithms in Graphbased Learning 
September 15, 2020 
Using Continuum Limits To Understand Data Clustering And Classification Franca Hoffmann (California Institute of Technology) 
Theory and Algorithms in Graphbased Learning 
September 18, 2020 
Learning IllConditioned Gaussian Graphical Models Raghu Meka (University of California, Los Angeles) 
Theory and Algorithms in Graphbased Learning 
September 15, 2020 
Graph Structure of Neural Networks Jure Leskovec (Stanford University) 
Theory and Algorithms in Graphbased Learning 
September 15, 2020 
On Anisotropic Diffusion Equations for Label Propagation LisaMaria Kreusser (University of Cambridge) 
Theory and Algorithms in Graphbased Learning 
September 16, 2020 
When Labelling Hurts: Learning to Classify LargeScale Data with Minimal Supervision Angelica AvilesRivero (University of Cambridge) 
Theory and Algorithms in Graphbased Learning 
September 16, 2020 
BiasVariance Tradeoffs in Joint Spectral Embeddings Daniel Sussman (Boston University) 
Theory and Algorithms in Graphbased Learning 
September 16, 2020 
Computational Complexity and Forbidden Graph Patterns Puck Rombach (University of Vermont) 
Theory and Algorithms in Graphbased Learning 
September 17, 2020 
LInfinity Variational Problems on Graphs: Applications and Continuum Limits Leon Bungert (FriedrichAlexanderUniversität ErlangenNürnberg), Tim Roith (FriedrichAlexanderUniversität ErlangenNürnberg) 
Theory and Algorithms in Graphbased Learning 
September 17, 2020 
Vertex Nomination, Consistent Estimation, and Adversarial Modification Vince Lyzinski (University of Maryland) 
Theory and Algorithms in Graphbased Learning 
October 29, 2019 
Highly Likely Clusterable Data With No Cluster Mimi Boutin (Purdue University) 
Data Science Seminar 
October 01, 2019 
Citizen Science and Machine Learning at Zooniverse Darryl Wright (University of Minnesota, Twin Cities) 
Data Science Seminar 
October 22, 2019 
Convergence Rates and Semiconvexity Estimates for the Continuum Limit of Nondominated Sorting Brendan Cook (University of Minnesota, Twin Cities) 
Data Science Seminar 
January 21, 2020 
Linear Unbalanced Optimal Transport Matthew Thorpe (University of Cambridge) 
Data Science Seminar 
February 18, 2020 
From Clustering with Graph Cuts to Isoperimetric Inequalities: Quantitative Convergence Rates of Cheeger Cuts on Data Clouds Nicolas Garcia Trillos (University of Wisconsin, Madison) 
Data Science Seminar 
January 28, 2020 
SEMINAR IS CANCELED  Machine Learning Meets Societal Values
Steven Wu (University of Minnesota, Twin Cities)  SEMINAR IS CANCELED

Data Science Seminar 
October 08, 2019 
Parallel Transport Convolutional Neural Networks on Manifolds Rongjie Lai (Rensselaer Polytechnic Institute) 
Data Science Seminar 
September 03, 2019 
Where and What to Look: Learning to Understand the Visual World in Autistic Brains Catherine Zhao (University of Minnesota, Twin Cities) 
Data Science Seminar 
September 24, 2019 
The Geometry of Ambiguity in Onedimensional Phase Retrieval Dan Edidin (University of Missouri) 
Data Science Seminar 
February 11, 2020 
Function Space MetropolisHastings Algorithms with NonGaussian Priors Bamdad Hosseini (California Institute of Technology) 
Data Science Seminar 
October 15, 2019 
Simple Approaches to Complicated Data Analysis Deanna Needell (University of California, Los Angeles) 
Data Science Seminar 
November 19, 2019 
Robust Representation for Graph Data Dongmian Zou (University of Minnesota, Twin Cities) 
Data Science Seminar 
August 18, 2020 
Fairness, Accountability, and Transparency: (Counter)Examples from Predictive Models in Criminal Justice Kristian Lum (University of Pennsylvania) 
Data Science Seminar 
December 03, 2019 
Exploiting Group and Geometric Structures for Massive Data Analysis Zhizhen (Jane) Zhao (University of Illinois at UrbanaChampaign) 
Data Science Seminar 
February 25, 2020 
Making Small Spaces Feel Large: Practical Illusions in Virtual Reality Evan Rosenberg (University of Minnesota, Twin Cities) 
Data Science Seminar 
February 04, 2020 
Different Aspects of Registration Problem Yuehaw Khoo (University of Chicago) 
Data Science Seminar 
November 26, 2019 
Information Flow and Security Aspects in 121 Networks Martina Cardone (University of Minnesota, Twin Cities) 
Data Science Seminar 
December 06, 2019 
Probabilistic Preference Learning with the Mallows Rank Model for Incomplete Data Arnoldo Frigessi (Oslo University Hospital) 
Data Science Seminar 
November 12, 2019 
Latent Factor Models for Largescale Data Xiaoou Li (University of Minnesota, Twin Cities) 
Data Science Seminar 
May 19, 2020 
Transmission Dynamics of Influenza and SARSCoV2: Environmental Determinants, Inference and Forecast Jeffrey Shaman (Columbia University) 
Data Science Seminar 
November 05, 2019 
Topics in Sparse Recovery via Constrained Optimization: Least Sparsity, Solution Uniqueness, and Constrained Exact Recovery Seyedahmad Mousavi (University of Minnesota, Twin Cities) 
Data Science Seminar 
September 17, 2019 
Optimal Recovery under Approximability Models, with Applications Simon Foucart (Texas A & M University) 
Data Science Seminar 
September 10, 2019 
Taming Nonconvexity: From Smooth to Nonsmooth Problems and Beyond Ju Sun (University of Minnesota, Twin Cities) 
Data Science Seminar 
June 09, 2020 
Data and Models for COVID19 DecisionMaking Forrest Crawford (Yale University) 
Data Science Seminar 
August 04, 2020 
A Network Science Approach for Controlling Epidemic Outbreaks Anil Vullikanti (University of Virginia) 
Data Science Seminar 
June 23, 2020 
Computational Science for COVID19 Pandemic Planning and Response Madhav Marathe (University of Virginia) 
Data Science Seminar 
March 03, 2020 
“Living” 3D World Models Leveraging Crowd Sourced Data JanMichael Frahm (Facebook) 
Data Science Seminar 
October 11, 2019 
Preparation for the MinneMUDAC Competition: Principals of Agricultural Economics I Tim Bodin (Cargill, Inc.) 
Industrial Problems Seminar 
October 11, 2019 
Preparation for the MinneMUDAC Competition: On Predicting Commodity Closing Prices for Soybeans Kylie Mitchell (Cargill, Inc.) 
Industrial Problems Seminar 
November 22, 2019 
Machine Learning Problems at Target Mauricio Flores (Target Corporation) 
Industrial Problems Seminar 
October 18, 2019 
Preparation for the MinneMUDAC Competition: Principals of Agricultural Economics II Tim Bodin (Cargill, Inc.) 
Industrial Problems Seminar 
October 11, 2019 
Data Science in Healthcare Yinglong Guo (UnitedHealth Group) 
Industrial Problems Seminar 
October 03, 2019 
Language and Interaction in Minecraft Arthur Szlam (Facebook) 
Industrial Problems Seminar 
May 22, 2020 
AI for COVID19: An Online Virtual Care Approach Xavier Amatriain (Curai) 
Industrial Problems Seminar 
November 15, 2019 
Pipelines, Graphs, and the Language of Shopping: Architecting Next Gen Machine Learning Capabilities for Retail Jonah White (Best Buy) 
Industrial Problems Seminar 
February 28, 2020 
Some Characteristics of Research in Finance Onur Ozyesil (Helm.ai) 
Industrial Problems Seminar 
October 25, 2019 
Gamma Guidance  The Mathematics Applied to a Launch Vehicle Gary Green (The Aerospace Corporation) 
Industrial Problems Seminar 
February 21, 2020 
Profiles of Math Careers in the Industry Martin Lacasse (ExxonMobil) 
Industrial Problems Seminar 
September 27, 2019 
Sampling from an Alternate Universe: Overview of Privacypreserving Synthetic Data Christine Task (Knexus Research Corporation) 
Industrial Problems Seminar 
January 31, 2020 
How NextEra Analytics Applies Math to Problems in Coupled Renewable and Energy Storage Systems Madeline Handschy (NextEra Analytics Inc) 
Industrial Problems Seminar 
September 16, 2019 
Functional data analysis for activity profiles from wearable devices Ian McKeague (Columbia University) 
Recent Progress in Foundational Data Science 
September 16, 2019 
Statistical Inference for HighDimensional Models via Recursive OnlineScore Estimation Runze Li (The Pennsylvania State University) 
Recent Progress in Foundational Data Science 
September 17, 2019 
Gaussian Complexity, Metric Entropy, and the Statistical Learning of Deep Nets Andrew Barron (Yale University) 
Recent Progress in Foundational Data Science 
September 17, 2019 
Nonlinear PDEs and regularization in machine learning Jeff Calder (University of Minnesota, Twin Cities) 
Recent Progress in Foundational Data Science 
September 16, 2019 
Envelope Methods Dennis Cook (University of Minnesota, Twin Cities) 
Recent Progress in Foundational Data Science 
September 16, 2019 
ML under a Modern Optimization Lens Dimitris Bertsimas (Massachusetts Institute of Technology) 
Recent Progress in Foundational Data Science 
June 26, 2019 
Very Tiny Knots in Nature
Kenneth Millett (University of California, Santa Barbara)

Public Lecture Series 
June 07, 2019 
Student Activity: Handson Statistics / MachineLearning Tutorial
Alicia Johnson (Macalester College)

Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop 
June 07, 2019 
Mentor Activity: Bringing Industrial Applications to the Mathematics Classroom Galin Jones (University of Minnesota, Twin Cities) 
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop 
June 07, 2019 
Make great money, change the world (for the better): PhD options in Management and Economics David Hummels (Purdue University) 
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop 
June 07, 2019 
Panel Discussion: Emerging Opportunities for Applied Statistics and Quantitative Finance PhDs 
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop 
June 07, 2019 
Student Activity: Finding your Focus in Graduate School Roy Araiza (Purdue University), Ty Frazier (University of Minnesota, Twin Cities), Richard McGehee (University of Minnesota, Twin Cities), Adrienne Sands (University of Minnesota, Twin Cities) 
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop 
June 07, 2019 
Mentor Activity: Creating an Action Plan Joe Omojola (Southern University at New Orleans), William Vélez (University of Arizona) 
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop 