Lulu Kang

Lulu Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. She previously held positions at Illinois Institute of Technology (Associate Professor, 2016–2023; Assistant Professor, 2010–2016). Her research focuses on statistical design of experiments, machine learning, uncertainty quantification, Bayesian statistics, and optimization, with applications in engineering, healthcare, and biophysics.

Education: Ph.D. and M.S. in Industrial Engineering/Operations Research from Georgia Institute of Technology (2005–2010), and B.S. in Mathematics from Nanjing University, China.

Research interests include optimal experimental design, Gaussian process modeling, variational inference, and interdisciplinary applications. She co-leads the 2025 Uncertainty Quantification and AI for Complex Systems program at the Institute for Mathematical and Statistical Innovation.

Key awards: SPAIG Award (ASA, 2020), NSF grants totaling over $400k, and recognition for early-career contributions. Active in editorial roles for Technometrics and SIAM/ASA Journal on Uncertainty Quantification.

Grants include NSF DMS awards on energetic variational inference and design of experiments, and interdisciplinary projects on antibiotic resistance forecasting and material science.

Her GitHub repository hosts code for Bayesian experimental design, reflecting her commitment to open science and practical applications of statistical methods.