Simon Mak

Assistant Professor of Statistical Science

Appointments and Affiliations

  • Assistant Professor of Statistical Science
  • Faculty Network Member of the Duke Institute for Brain Sciences

Contact Information

  • Office Location: 214 Old Chemistry, Box 90251, Durham, NC 27708-0251
  • Email Address: sm769@duke.edu
  • Websites:

Education

  • B.S. Simon Fraser University, 2013
  • M.S. Georgia Institute of Technology, 2018
  • Ph.D. Georgia Institute of Technology, 2018

Courses Taught

  • STA 995: Internship
  • STA 993: Independent Study
  • STA 891: Topics for Preliminary Exam Preparation in Statistical Science
  • STA 790-1: Special Topics in Statistics
  • STA 701S: Statistical Science Graduate Research Seminar
  • STA 693: Research Independent Study
  • STA 325L: Machine Learning and Data Mining
  • STA 240L: Probability for Statistical Inference, Modeling, and Data Analysis
  • MATH 228L: Probability for Statistical Inference, Modeling, and Data Analysis

Representative Publications

  • Ehlers, R., Y. Chen, J. Mulligan, Y. Ji, A. Kumar, S. Mak, P. M. Jacobs, et al. “Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements.” Physical Review C 111, no. 5 (May 1, 2025). https://doi.org/10.1103/PhysRevC.111.054913.
  • Narayanan, S. R., Z. Sun, S. Yang, J. J. Miller, S. Mak, K. S. Kim, and C. B. M. Kweon. “Local-Transfer Gaussian Process (LTGP) Learning for Multi-fuel Capable Engines.” In AIAA Science and Technology Forum and Exposition AIAA Scitech Forum 2025, 2025. https://doi.org/10.2514/6.2025-0790.
  • Li, K., and S. Mak. “ProSpar-GP: Scalable Gaussian Process Modeling with Massive Nonstationary Datasets.” Journal of Computational and Graphical Statistics, January 1, 2025. https://doi.org/10.1080/10618600.2025.2490264.
  • Tachibana, Y., A. Kumar, A. Majumder, A. Angerami, R. Arora, S. A. Bass, S. Cao, et al. “Hard jet substructure in a multistage approach.” Physical Review C 110, no. 4 (October 1, 2024). https://doi.org/10.1103/PhysRevC.110.044907.
  • Hahn, S., J. Yin, R. Zhu, W. Xu, Y. Jiang, S. Mak, and C. Rudin. “SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 5050–60, 2024. https://doi.org/10.1145/3637528.3671626.