Simon Mak

Assistant Professor

Department of Statistical Science

Duke University

Office: Old Chemistry 112A

E-mail: sm769[at]duke.edu

Google Scholar, Curriculum Vitae

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. Prior to Duke, I was a Postdoctoral Fellow at the Stewart School of Industrial & Systems Engineering at Georgia Tech.

My research involves integrating domain knowledge (e.g., scientific theories, mechanistic models, guiding principles) as prior information for statistical inference and prediction. 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 research tackles methodological, theoretical, and algorithmic challenges in this integration. This involves building probabilistic models on complex objects (e.g., functions, manifolds, networks), and developing efficient learning algorithms and data collection methods. Current research is motivated from ongoing projects in nuclear physics, aerospace engineering, material science, and finance.

Recent news

  • 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!

  • October 2022: Xiaojun Zheng was awarded the 2022 INFORMS Data Mining Best Student Paper Award for our paper "PERCEPT: a new online change-point detection method using topological data analysis". Congrats Xiaojun!

  • July 2022: Flora Shi received the BayesComp Best Poster Award at ISBA 2022 for our paper "ESPs: a new cost-efficient sampler for expensive posterior distributions". Congrats Flora!

  • May 2022: Our paper "GPSigma: Gaussian process prediction of covariance matrices, with applications to emulation of heavy-ion collisions" (joint work with Ruda Zhang and David Dunson) was a runner-up for the 2022 IISE-DAIS Best Paper Track Award. Details at https://www.iise.org/details.aspx?id=872.

  • April 2022: Interview with Glen Wright Colopy on "Integrating Science into Stats Models" at https://www.youtube.com/watch?v=bUbZO7R4z40. Great conversation on a very important and timely topic!