Chenlu Shi

Assistant Professor, Mathematical Sciences
About Me
Chenlu’s research centers on data collection methodologies, big data analysis, and deep learning. Her current work delves into the theory and application of factorial designs for physical experiments, space-filling designs for computer experiments, big data reduction methods, and hyperparameter optimization in deep learning. Prior to joining NJIT, she was an Assistant Professor in the Department of Statistics at Colorado State University. Before that, she was an Assistant Adjunct Professor in the Department of Statistics at the University of California, Los Angeles.
Education
Ph.D.; Simon Fraser University; Statistics; 2019
M.S.; Simon Fraser University; Statistics; 2015
B.S.; St. Francis Xavier University; Mathematics; 2013
Website
https://sites.google.com/view/chenlus
Past Courses
MATH 344: REGRESSION ANALYSIS
MATH 478: STAT METHODS IN DATA SCI
MATH 699: DESIGN AND ANALYSIS OF EXPERIMENTS
Research Interests
Big Data Reduction and Analysis; Computer Experiments; Experimental Design; Hyperparameter Optimization for Deep Learning
Journal Article
Chenlu Shi, Ye Tian, Hongquan Xu. 2026. "Results on large strong orthogonal arrays of strength three." .
Chenlu Shi, Hongquan Xu. 2023. "A projection space-filling criterion and related optimality results." Journal of the American Statistical Association, vol. 119, pp. 2658-2669.
Guanzhou Chen, Chenlu Shi, Boxin Tang. 2023. "Nonregular designs from Paley’s Hadamard matrices: Generalized resolution, projectivity and hidden projection property." Electronic Journal of Statistics, vol. 17, no. 2, pp. 2120-2138.
Chenlu Shi, Boxin Tang. 2023. "On construction of nonregular two-level factorial designs with maximum generalized resolutions." Statistica Sinica, vol. 33, no. 2, pp. 593-607.
Chenlu Shi, Ashley Kathleen Chiu, Hongquan Xu. 2023. "Evaluating designs for hyperparameter tuning in deep neural networks." New England Journal of Statistics in Data Science, vol. 1, no. 3, pp. 334–341.
Chenlu Shi, Boxin Tang. 2021. "Model-robust subdata selection for big data." Journal of Statistical Theory and Practice, vol. 15, pp. 1-17.