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Data Science Seminar
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
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