Roshan Vengazhiyil Joseph

Roshan Vengazhiyil Joseph is the A. Russell Chandler III Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. He holds a Ph.D. in Statistics from the University of Michigan (2002), an M.Tech. in Quality, Reliability, and Operations Research from the Indian Statistical Institute (1995), and a B.Tech. in Product Engineering and Management from the National Institute of Technology, Calicut (1993).

• Ph.D. Statistics (2002), University of Michigan
• M.Tech. Quality, Reliability, and Operations Research (1995), Indian Statistical Institute
• B.Tech. Product Engineering and Management (1993), National Institute of Technology-Calicut

Dr. Joseph's research focuses on applied and computational statistics, with emphasis on developing data analytic methods for engineering problems. His expertise spans Bayesian computation, uncertainty quantification, Gaussian process modeling, and quality engineering. Recent work includes active learning strategies, calibration of micromechanical models, and machine learning applications in materials science.

His research trends include Bayesian optimization, Gaussian process modeling, experimental design, uncertainty quantification, machine learning for industrial applications, and computational efficiency in statistical methods.

• Distinguished Dissertation Award (2003)
• CAREER Award (NSF, 2005)
• Jack Youden Prize (ASQ, 2005)
• Franz Edelman Laureate (INFORMS, 2017)
• Statistics in Physical & Engineering Sciences Award (ASA, 2019)
• SPAIG Award (ASA, 2020)
• Lloyd S. Nelson Award (ASQ, 2021)
• Wilcoxon Award (ASQ, 2023)
• Distinguished Alumni Award (NIT-Calicut, 2024)

As a Fellow of the American Statistical Association (2012) and the American Society for Quality (2020), Dr. Joseph has contributed to statistical methodology in industrial engineering. He served as Editor of Technometrics (2020-2022) and developed software packages like first, TAG, and SFDesign. His work bridges statistical theory with practical applications in manufacturing, energy systems, and materials science.