Chenlu Shi

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.

Ph.D.; Simon Fraser University; Statistics; 2019

M.S.; Simon Fraser University; Statistics; 2015

B.S.; St. Francis Xavier University; Mathematics; 2013

https://sites.google.com/view/chenlus

Big Data Reduction and Analysis; Computer Experiments; Experimental Design; Hyperparameter Optimization for Deep Learning

Shi, Chenlu, & Xu, Hongquan (2023). A projection space-filling criterion and related optimality results. Journal of the American Statistical Association, 1–23.

Chen, Guanzhou, & Shi, Chenlu, & Tang, Boxin (2023). Nonregular designs from Paley’s Hadamard matrices: Generalized resolution, projectivity and hidden projection property. Electronic Journal of Statistics, 17(2), 2120-2138.

Shi, Chenlu, & Tang, Boxin (2023). On construction of nonregular two-level factorial designs with maximum generalized resolutions. Statistica Sinica, 33(2), 593-607.

Shi, Chenlu, & Chiu, Ashley Kathleen, & Xu, Hongquan (2023). Evaluating designs for hyperparameter tuning in deep neural networks. New England Journal of Statistics in Data Science, 1(3), 334–341.

Shi, Chenlu, & Tang, Boxin (2021). Model-robust subdata selection for big data. Journal of Statistical Theory and Practice, 15, 1-17.