SURE 2022

Instructors

• Director, Instructor: Ron Yurko (ryurko@andrew.cmu.edu) is an incoming Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University. He received his PhD in Statistics at CMU in 2022 under supervision of Professor Kathryn Roeder and Professor Max G’Sell. His research focuses on developing methods at the interface of inference and machine learning, oriented towards problems in statistical genetics and sports analytics. Previously, he received his BS in Statistics (also) at CMU in 2015, briefly worked in finance, as well as with the Pittsburgh Pirates and Zelus Analytics.

• Teaching Assistant: Nicholas Kissel (nkissel@andrew.cmu.edu) is a third year statistics PhD student at CMU. He received an MS in statistics and BS in math & statistics from Pitt in 2019. He is am primarily interested in developing methods for generating model confidence sets. More broadly, he is interested in creating inferential procedures for machine learning modeling methods, as well as developing accessible statistical tools that are applicable to natural and social science research.

• Teaching Assistant: Meg Ellingwood (mellingw@andrew.cmu.edu) is a first-year student in the PhD program in statistics at CMU. She graduated from Kenyon College and briefly taught high school statistics before entering graduate school. Meg participated in CMSACamp Summer 2020 and is very excited to be involved again this year. She is interested in applications of statistics and data science in sports, public health, and neuroscience.

• Teaching Assistant: Wanshan Li (wanshanl@andrew.cmu.edu) is a fifth year PhD student in Statistics at CMU. He received a BS in Maths & Applied Maths from Peking University in 2017. He had research experience on network and text data analysis, especially on community detection and topic modelling, and currently he is working with Professor Alessandro Rinaldo on ranking, change point analysis, and other related problems in high-dimensional statistics.

• Teaching Assistant: Kenta Takatsu (ktakatsu@andrew.cmu.edu) is a first year PhD student in Statistics at CMU. He received a BS in Computer Science from Cornell University in 2019. Before transferring to CMU, he was a graduate student at UMass Amherst where he studied Math & Statistics. His primary research interest is in empirical processes and their applications to non/semiparametric inference on functionals under shape constraints.

• Teaching Assistant: YJ Choe (yjchoe@andrew.cmu.edu) is a fourth-year joint Ph.D. student in Statistics and Machine Learning at Carnegie Mellon University, working with Prof. Aaditya Ramdas. His research interests include game-theoretic statistical methods for anytime-valid sequential inference, statistical forecasting, and sequential decision making. YJ is a casual sports fan, and his work in forecasting is often motivated by its applications to sports. Previously, he worked as a Research Scientist at Kakao Brain and Kakao, where he specialized in deep learning for natural language processing, and he received an M.S. in Machine Learning at CMU as well as a B.S. in Mathematics and Computer Science at the University of Chicago.

Program description

The program will concentrate on statistics and data science methodology with applications in sports and health analytics. All lecture slides will be available at the Lectures tab, and all lab / demo materials will be available at the sports and health.