Scope: This is a free virtual summer school aiming to promote the studies and research activities on random optimizations in complex systems for undergraduate students. The topics will cover a wide range of subjects and tools in probability theory and mathematical physics, especially addressing their applications in machine learning and data science. During the 8-day program, the students are expected to attend two lecture sessions and a group problem session everyday. Additional professional development sessions will discuss graduate school and careers in the related fields. Upon the completion, students will receive a certificate issued by the School of Mathematics at the University of Minnesota. The program is financially supported by the National Science Foundation (NSF).
Who can apply: Undergraduate students who have received relevant training in introductory probability theory and linear algebra at undergraduate level. While the priority will be given to undergraduate students, we also encourage the involvement of the first year graduate students. Time: June 21-25 and June 28-30, 2021 Meeting schedule: The summer school will be held remotely via Zoom. Two 75-minute main lecturers everyday: 10:30am-11:45am and 1:00pm-2:15pm (CST) One 75-minute problem section every day: 2:30pm-3:45pm (CST) Main lectures and topics: 1. Jeff Calder: Partial Differential Equations and Graph Based Learning 2. Wei-Kuo Chen: Statistical Physics and Random Optimizations 3. William Leeb: Applications of Random Matrix Theory to Data Analysis 4. Arnab Sen: Sparse Recovery and Community Detection Application: Start: March 8, 2021 Deadline: May 15, 2021 Application materials: 1. Your Resume 2. A short recommendation letter from a professor Contact: If you should have any questions, please feel free to contact, Wei-Kuo Chen, via email: wkchen@umn.edu |