Campuses:

Talks

Date Title / Speaker Eventsort ascending
November 11, 2020 A Fast Approach to Optimal Transport: The Back-and-forth 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
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 Graph-based Learning
September 18, 2020 Learning Restricted Boltzmann Machines
Ankur Moitra (Massachusetts Institute of Technology)
Theory and Algorithms in Graph-based Learning
September 18, 2020 Learning Discrete Graphical Models: Exact & Neural Network Assisted Methods
Marc Vuffray (Los Alamos National Laboratory)
Theory and Algorithms in Graph-based Learning
September 14, 2020 A Self-avoiding Approximate Mean Curvature Flow
Simon Masnou (Université Claude-Bernard (Lyon I))
Theory and Algorithms in Graph-based Learning
September 14, 2020 Reducibility and Statistical-Computational Gaps from Secret Leakage
Guy Bresler (Massachusetts Institute of Technology)
Theory and Algorithms in Graph-based Learning
September 14, 2020 Robust Group Synchronization via Cycle-Edge Message Passing
Gilad Lerman (University of Minnesota, Twin Cities)
Theory and Algorithms in Graph-based Learning
September 15, 2020 Using Continuum Limits To Understand Data Clustering And Classification
Franca Hoffmann (California Institute of Technology)
Theory and Algorithms in Graph-based Learning
September 18, 2020 Learning Ill-Conditioned Gaussian Graphical Models
Raghu Meka (University of California, Los Angeles)
Theory and Algorithms in Graph-based Learning
September 15, 2020 Graph Structure of Neural Networks
Jure Leskovec (Stanford University)
Theory and Algorithms in Graph-based Learning
September 15, 2020 On Anisotropic Diffusion Equations for Label Propagation
Lisa-Maria Kreusser (University of Cambridge)
Theory and Algorithms in Graph-based Learning
September 16, 2020 When Labelling Hurts: Learning to Classify Large-Scale Data with Minimal Supervision
Angelica Aviles-Rivero (University of Cambridge)
Theory and Algorithms in Graph-based Learning
September 16, 2020 Bias-Variance Tradeoffs in Joint Spectral Embeddings
Daniel Sussman (Boston University)
Theory and Algorithms in Graph-based Learning
September 16, 2020 Computational Complexity and Forbidden Graph Patterns
Puck Rombach (University of Vermont)
Theory and Algorithms in Graph-based Learning
September 17, 2020 L-Infinity Variational Problems on Graphs: Applications and Continuum Limits
Leon Bungert (Friedrich-Alexander-Universität Erlangen-Nürnberg), Tim Roith (Friedrich-Alexander-Universität Erlangen-Nürnberg)
Theory and Algorithms in Graph-based Learning
September 17, 2020 Vertex Nomination, Consistent Estimation, and Adversarial Modification
Vince Lyzinski (University of Maryland)
Theory and Algorithms in Graph-based Learning
September 17, 2020 Community Detection Using Total Variation and Surface Tension
Zach Boyd (University of North Carolina, Chapel Hill)
Theory and Algorithms in Graph-based Learning
September 14, 2020 A Fast Graph-Based Data Classification Method with Applications to 3D Sensory Data in the Form of Point Clouds
Ekaterina Rapinchuk (Michigan State University)
Theory and Algorithms in Graph-based Learning
September 15, 2020 Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization
Yifei Lou (University of Texas at Dallas)
Theory and Algorithms in Graph-based Learning
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
October 08, 2019 Parallel Transport Convolutional Neural Networks on Manifolds
Rongjie Lai (Rensselaer Polytechnic Institute)
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
September 24, 2019 The Geometry of Ambiguity in One-dimensional Phase Retrieval
Dan Edidin (University of Missouri)
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
February 11, 2020 Function Space Metropolis-Hastings Algorithms with Non-Gaussian 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
February 25, 2020 Making Small Spaces Feel Large: Practical Illusions in Virtual Reality
Evan Rosenberg (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 Urbana-Champaign)
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 1-2-1 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 Large-scale Data
Xiaoou Li (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
May 19, 2020 Transmission Dynamics of Influenza and SARS-CoV-2: 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 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 COVID-19 Decision-Making
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 COVID-19 Pandemic Planning and Response
Madhav Marathe (University of Virginia)
Data Science Seminar
March 03, 2020 “Living” 3D World Models Leveraging Crowd Sourced Data
Jan-Michael Frahm (Facebook)
Data Science Seminar
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 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
February 28, 2020 Some Characteristics of Research in Finance
Onur Ozyesil (Helm.ai)
Industrial Problems Seminar
May 22, 2020 AI for COVID-19: 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
September 27, 2019 Sampling from an Alternate Universe: Overview of Privacy-preserving Synthetic Data
Christine Task (Knexus Research Corporation)
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
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
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
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
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 High-Dimensional Models via Recursive Online-Score 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
June 26, 2019 Very Tiny Knots in Nature

Kenneth Millett (University of California, Santa Barbara)

Public Lecture Series
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
June 06, 2019 Introduction and Overview
William Vélez (University of Arizona)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 Data Science: What is it? Why is everyone talking about it? Should you be doing it? (You probably are already)
Rebecca Nugent (Carnegie Mellon University)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 Student Activity: Support Your vocals like Destiny's Child supported Beyonce
Joy Dolo (Actor and Writer), John Gebretatose (Actor and Writer)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 The Quite Reasonable Effectiveness of Mathematical Sciences
Juan Meza (National Science Foundation)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 Welcome Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 Panel Discussion: Emerging Opportunities for Applied Mathematics and Data Science PhDs Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 Student Activity: I know you are, but what am I?
Joy Dolo (Actor and Writer), John Gebretatose (Actor and Writer)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 06, 2019 Mentor Activity: Attracting and Mentoring Students in A Multicultural Context
Helena Noronha (California State University Northridge), Joe Omojola (Southern University at New Orleans)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 07, 2019 On some examples of statistical applications
Javier Rojo (Oregon State University)
Career Paths in the Mathematical Sciences: An IMA / Math Alliance Workshop
June 07, 2019 Student Activity: Hands-on Statistics / Machine-Learning 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
October 15, 2019 Computational Imaging with Deep Learning
Orazio Gallo (NVIDIA Corporation)
Computational Imaging
October 18, 2019 Computational Radar Imaging
Mujdat Cetin (University of Rochester)
Computational Imaging
October 16, 2019 Welcome Remarks Computational Imaging
October 18, 2019 Challenges and Opportunities in Magnetic Resonance Fingerprinting
Nicole Seiberlich (University of Michigan)
Computational Imaging
October 16, 2019 Coherent Optical Processing with Machine Learning
Charles Bouman (Purdue University)
Computational Imaging
October 18, 2019 Q&A/Closing Remarks
Brendt Wohlberg (Los Alamos National Laboratory)
Computational Imaging
October 16, 2019 Faster Guaranteed GAN-based recovery in Linear Inverse Problems
Yoram Bresler (University of Illinois at Urbana-Champaign)
Computational Imaging
October 14, 2019 Welcome Remarks Computational Imaging
October 16, 2019 Geometry of Convolutional Neural Networks for Computational Imaging
Jong Chul Ye (Korea Advanced Institute of Science and Technology (KAIST))
Computational Imaging
October 17, 2019 Computational Imaging: Beyond the limits imposed by lenses
Ashok Veeraraghavan (Rice University)
Computational Imaging
October 14, 2019 Computational Microscopy
Laura Waller (University of California, Berkeley)
Computational Imaging

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