Rui Tuo

Rui Tuo is an Assistant Professor in the Department of Industrial & Systems Engineering at Texas A&M University. He is also affiliated with the Department of Statistics and serves as a Texas A&M Institute of Data Science Career Initiation Fellow. His research focuses on data science, computer experiments, uncertainty quantification, probabilistic machine learning models, and nonparametric statistics.

Dr. Tuo holds a Ph.D. in Statistics from the University of Chinese Academy of Sciences (2013). His work emphasizes methodological advancements in statistical modeling and computational efficiency, particularly in high-dimensional and complex systems.

His research interests include Gaussian process models, computer model calibration, kernel methods, and their applications in engineering and data-driven decision-making. Recent publications explore topics like multi-fidelity optimization, privacy-aware machine learning, and distributed Gaussian processes.

Key achievements include the Career Initiation Fellowship (2022) and contributions to foundational works in calibration theory and uncertainty quantification. His research bridges statistical theory and practical applications, addressing challenges in simulation-based inference and scalable algorithms.