Simon Mak
Assistant Professor
Department of Statistical Science
Office: Old Chemistry 112A
E-mail: sm769[at]duke.edu
Assistant Professor
Department of Statistical Science
Office: Old Chemistry 112A
E-mail: sm769[at]duke.edu
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
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.
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.