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Mathematics of Machine Learning
Recent advancements of machine learning methods lave led to breakthroughs in a wide range of applications. In spite of their empirical successes, the theoretical understanding of machine learning techniques are far from complete. New tools from mathematics and statistics have been showing their power in explaining the mystery and more will be emerging. The purpose of this reading seminar is threefold: First to discuss recent research works that lie in the interface of machine learning, mathematics, statistics and etc; second to expose new research questions and foster collaborations between different groups and even different departments; and third to create new research opportunities for junior researchers and graduate students. All faculty, VAPs, graduate students are welcome to join.
Date  Speaker  Title 

Fri. 9/15

Wuchen Li University of South Carolina 
Information Gamma calculus: Convexity analysis for stochastic differential equations 
Fri. 9/22

Jack Xin University of California, Irvine 
Coarse Gradient Descent Method and Quantization of Deep Neural Networks 
Fri. 9/29

Siavash Jafarzadeh Lehigh University 
Reliable datadriven models for engineering design 
Fri. 10/13

Abiy Tasissa Tufts University 
TBA 
Fri. 10/20

Nan Chen University of WisconsinMadison 
TBA 
Fri. 10/27

Haizhao Yang University of Maryland, College Park 
DiscretizationInvariant Operator Learning: Algorithms and Theory 
Fri. 11/3

Thomas Strohmer University of California, Davis 
TBA 
Fri. 11/17

Dejan Slepčev Carnegie Mellon University 
TBA 
Fri. 12/1

Maarten de Hoop Rice University 
TBA 
Department of Mathematics and Statistics