Simon Mak

Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences.

His educational background includes:

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

Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling, Bayesian inference, Gaussian process emulation, and uncertainty quantification. He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing.

Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact.

Dr. Mak leads multiple significant research initiatives:

• Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026)
• Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025)
• The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025)

These projects fund his development of statistical frameworks for scientific discovery in complex systems.

He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.