Data Science Seminar

August 10, 2018 - May 27, 2019

Content

Overview

Organizers:

Schedule

Friday, August 10, 2018
			Video
3:30 pm - 4:30 pm	Mathematics of Big Data & Machine Learning Jeremy Kepner (Massachusetts Institute of Technology)	Lind 305

Monday, September 17, 2018
			Video
1:25 pm - 2:25 pm	The Role of the Translation Distribution in Multi-reference Alignment William Leeb (University of Minnesota, Twin Cities)	Lind 305

Monday, September 24, 2018
			Video
1:25 pm - 2:25 pm	Genetics of mRNA and Protein Expression in Large Yeast Populations Frank Albert (University of Minnesota, Twin Cities)	Lind 305

Monday, October 01, 2018
			Video
1:25 pm - 2:25 pm	Scalable Collaboration for Data Science Done in Open Source Tools and Frameworks Tyler Whitehouse (Gigantum)	Lind 305

Monday, October 08, 2018
			Video
1:25 pm - 2:25 pm	A PDE Approach to a Prediction Problem Involving Randomized Strategies Nadejda Drenska (University of Minnesota, Twin Cities)	Lind 305

Monday, October 15, 2018
			Video
1:25 pm - 2:25 pm	Free Component Analysis Raj Nadakuditi (University of Michigan)	Keller 3-180

Monday, October 22, 2018
			Video
1:25 pm - 2:25 pm	Large Sample Asymptotics of Graph-based Methods in Machine Learning: Mathematical Analysis and Implications Nicolas Garcia-Trillos (University of Wisconsin, Madison)	Lind 305

Monday, October 29, 2018
			Video
1:25 pm - 2:25 pm	A Picture of the Energy Landscape of Deep Neural Networks Pratik Chaudhari (California Institute of Technology)	Lind 305

Monday, November 05, 2018
			Video
1:25 pm - 2:25 pm	Solving Infinite Dimensional Optimization Problems with Convergence Guarantees Matthew Jacobs (University of California, Los Angeles)	Lind 305

Monday, November 12, 2018
			Video
1:25 pm - 2:25 pm	Learning from Highly Correlated Features using Graph Total Variation Rebecca Willett (University of Chicago)	Lind 305

Monday, November 19, 2018
			Video
1:25 pm - 2:25 pm	Lipschitz Regularized Deep Neural Networks Converge and are Robust to Adversarial Perturbations Adam Oberman (McGill University)	Lind 305

Monday, November 26, 2018
			Video
1:25 pm - 2:25 pm	Solving PDEs with Deep Learning Lexing Ying (Stanford University)	Lind 305

Wednesday, November 28, 2018
			Video
1:25 pm - 2:25 pm	Convex Relaxation Approaches for Strictly Correlated Density Functional Theory Lexing Ying (Stanford University)	Lind 305

Thursday, November 29, 2018
			Video
3:35 pm - 4:35 pm	Interpolative Decomposition and its Applications (Math Dept Colloquium Lecture) Lexing Ying (Stanford University)	Vincent 16

Monday, December 10, 2018
			Video
1:25 pm - 2:25 pm	Stratifying High-Dimensional Data Based on Proximity to the Convex Hull Boundary Lori Ziegelmeier (Macalester College)	Lind 305

Monday, January 28, 2019
			Video
1:25 pm - 2:25 pm	Data-Driven Distributionally Robust Appointment Scheduling Guanglin Xu (University of Minnesota, Twin Cities)	Lind 305

Monday, February 04, 2019
			Video
1:25 pm - 2:25 pm	Gromov-Monge Quasi Metrics and Distance Distributions Tom Needham (The Ohio State University)	Lind 305

Monday, February 11, 2019
			Video
1:25 pm - 2:25 pm	Style Transfer by Relaxed Optimal Transport and Self-Similarity Greg Shakhnarovich (Toyota Technological Institute at Chicago)	Lind 305

Monday, February 18, 2019
			Video
1:25 pm - 2:25 pm	Graph Convolutional Neural Network via Scattering Dongmian Zou (University of Minnesota, Twin Cities)	Lind 305

Monday, February 25, 2019
			Video
1:25 pm - 2:25 pm	Strong Coresets for k-Median and Subspace Approximation: Goodbye Dimension David Woodruff (Carnegie Mellon University)	Lind 305

Monday, March 04, 2019
			Video
1:25 pm - 2:25 pm	Peculiar Properties of Locally Linear Embedding -- Toward Theoretical Understanding of Unsupervised Learning Hau-tieng Wu (Duke University)	Lind 305

Monday, March 11, 2019
			Video
1:25 pm - 2:25 pm	Robust and Phaseless PCA (and Subspace Tracking) Namrata Vaswani (Iowa State University)	Lind 305

Monday, March 25, 2019
			Video
1:25 pm - 2:25 pm	Recommendation Systems in Real Life Mark Hsiao (Netflix, Inc)	Lind 305

Monday, April 01, 2019
			Video
1:25 pm - 2:25 pm	How to Deal with Big Data? Understanding Large-scale Distributed Regression Edgar Dobriban (Wharton School of the University of Pennsylvania)	Lind 305

Monday, April 08, 2019
			Video
1:25 pm - 2:25 pm	A Brief Overview of Quantum Computing Vlad Pribiag (University of Minnesota, Twin Cities)	Lind 305

Monday, April 15, 2019
			Video
1:25 pm - 2:25 pm	Recent Advances in Wasserstein Distributionally Robust Optimization Rui Gao (The University of Texas at Austin)	Lind 305

Monday, April 22, 2019
			Video
1:25 pm - 2:25 pm	How Hard is it to Fool a Neural Net? A Mathematical Look at Adversarial Examples Tom Goldstein (University of Maryland)	Lind 305

Monday, April 29, 2019
			Video
1:25 pm - 2:25 pm	Solving Multiscale Problems with Subsampled Data by Integrating PDE Analysis with Data Science Thomas Hou (California Institute of Technology)	Lind 305

Monday, May 06, 2019
			Video
1:25 pm - 2:25 pm	Robust Accelerated Gradient Methods Mert Gurbuzbalaban (Rutgers, The State University Of New Jersey)	Lind 305