학부생 여러분께,

미국 미네소타대학 수학부에서 진행하는 온라인 여름학교 프로그램을 아래와 같이 안내드립니다.

2021년 온라인 서머스쿨: 최적화의 랜덤구조와 응용

주제

학부생을 위한 온라인 서머스쿨로 '복잡계의 랜덤 최적화(random optimizations in complex systems)'에 관한 주제로 확률론과 수리물리 분야의 다양한 주제와 머신러닝, 데이터 과학에서의 응용을 다룰 예정임. 8일동안 줌(Zoom)으로 개최되며, 매일 2개의 강의와 그룹 문제토론을 가짐(대학원 진학 및 관련 진로에 대한 토론도 있을 예정). 과정을 다 마친 경우, 미네소타대학 수학부에서 수료증을 받게 됨. 수업료는 없음.

신청: 확률론과 선형대수학을 이수한 학부생이면 누구나 참여 가능. 참여를 원할 경우, 본인의 1) 이력서, 2) 지도교수 추천서를  MathPrograms.org 보내면 됨.

기간: 2021.6.21~25, 2021.6.28~30

기타 상세한 사항은 아래 영문을 참조하시길 바랍니다. 질문이 있을 경우에는 Wei-Kuo Chen, via email: wkchen@umn.edu로 해주시면 됩니다.

학부생의 많은 관심 부탁드립니다.

수리과학과사무실 드림

 
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 related fields. Upon the completion, students will receive a certificate issued by the School of Mathematics at the University of Minnesota. This program is financially supported by the National Science Foundation.

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 the undergraduate students, we also encourage the involvement of the first year graduate students.

Time: June 21-25 and June 28-30, 2021

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-minue problem section everyday: 2:30pm-3:45pm (CST)
  
• Main lecturers and topics:
  
  

  Name	Topic
  Jeff Calder	Partial Differential Equations and Graph Based Learning
  Wei-Kuo Chen	Statistical Physics and Random Optimizations
  William Leeb	Applications of Random Matrix Theory to Data Analysis
  Arnab Sen	Sparse Recovery and Community Detection

Application:

• Start: March 8, 2021
• Deadline: May 15, 2021
• Application materials:
  1. A brief CV
  2. A short recommendation letter from a professor
• Please apply through MathPrograms.org

Contact: If you have any questions, please feel free to contact, Wei-Kuo Chen, via email: wkchen@umn.edu