Simon Mak
Assistant Professor
Department of Statistical Science
Office: Old Chemistry 112A
E-mail: sm769[at]duke.edu
Education
Ph.D. in Industrial Engineering (2018), Georgia Institute of Technology
M.Sc. in Statistics (2018), Georgia Institute of Technology
B.Sc. in Statistics and Actuarial Science (2013), Simon Fraser University
About me
I am an Assistant Professor in the Department of Statistical Science at Duke University.
My research involves integrating domain knowledge (e.g., scientific theories, mechanistic models, guiding principles) as prior information for cost-efficient statistical inference, prediction and decision-making. This gives a holistic framework for interpretable statistical learning, providing a principled way for scientists to validate theories from data, and for statisticians to integrate scientific knowledge. My ongoing research is motivated from interdisciplinary collaborations in high-energy and nuclear physics, aerospace engineering and public policy. I am currently the Program Chair-Elect of the ASA Section on Physical and Engineering Sciences, the Deputy Spokesperson of JETSCAPE (a multi-institutional collaboration on high-energy physics), and an Associate Editor for Technometrics and Data Science in Science. I have been honored to receive the Blackwell-Rosenbluth Award, the ASA SPES Award, the ASA Editor's Choice Collection Award, and best paper awards from the ASA, INFORMS and IISE.
Recent news
January 2024: Our paper ""PERCEPT: a new online change-point detection method using topological data analysis" was selected (one of two articles) by the editors for an outstanding Technometrics article in the ASA Choice Collection issue [link].
August 2023: Greatly appreciative of new funding from NSF DMS 2220496, NSF DMS 2316012, and DE-SC0024477 on a variety of projects on threat detection, Quasi Monte Carlo sampling, and Bayesian uncertainty quantification.
August 2023: Xiaojun Zheng was awarded the 2023 ASA Section on Physical and Engineering Sciences Best Student Paper Award for our paper "PERCEPT: a new online change-point detection method using topological data analysis". Congrats Xiaojun!
May 2023: Flora Shi received the 2023 Undergraduate BEST Award, awarded to the best senior thesis in the Department of Statistical Science at Duke. Congrats Flora!
November 2022: Honored to receive the Blackwell-Rosenbluth Award from j-ISBA, which recognizes outstanding junior Bayesian researchers for their contributions to the field and community.