Associate Professor of Statistical Science
Appointments and Affiliations
- Associate 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
- Ph.D. Georgia Institute of Technology, 2018
- M.S. 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 693: Research Independent Study
- STA 643: Modern Design of Experiments
- STA 493: 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
- Tachibana, Y., C. Sirimanna, A. Majumder, A. Angerami, R. Arora, S. A. Bass, Y. Chen, et al. “Effect of recoils on soft-drop-groomed observables in γ-tagged jets in a multistage approach.” Physical Review C 113, no. 3 (March 28, 2026). https://doi.org/10.1103/7QXY-FZ41.
- Li, K., S. Mak, J. F. Paquet, and S. A. Bass. “Additive Multi-Index Gaussian Process Modeling, with Application to Multi-Physics Surrogate Modeling of the Quark-Gluon Plasma.” Journal of the American Statistical Association 121, no. 553 (January 1, 2026): 44–59. https://doi.org/10.1080/01621459.2025.2529025.
- Wang, X., S. Mak, J. Miller, and J. Wu. “Local Transfer Learning Gaussian Process Modeling, with Applications to Surrogate Modeling of Expensive Computer Simulators.” SIAM Asa Journal on Uncertainty Quantification 14, no. 1 (January 1, 2026): 256–86. https://doi.org/10.1137/24M1703057.
- Ni-Hahn, S., R. Zhu, J. Yin, Y. Jiang, C. Rudin, and S. Mak. “AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation.” In Proceedings of the Aaai Conference on Artificial Intelligence, 40:24567–75, 2026. https://doi.org/10.1609/aaai.v40i29.39640.
- Roch, H., G. Pihan, A. Monnai, S. Ryu, N. Senthilkumar, J. Staudenmaier, H. Elfner, et al. “Transport-based initial conditions for heavy-ion collisions at finite densities.” Physical Review C 113, no. 2 (January 1, 2026). https://doi.org/10.1103/PVVM-QGCD.