WANG Di

WANG Di is an Associate Professor in the Department of Industrial Engineering and Management at Shanghai Jiao Tong University. His research focuses on statistical analysis and artificial intelligence for modeling and monitoring complex engineering systems, including statistical modeling, prediction, and monitoring of spatiotemporal dynamic systems, as well as deep learning and multisensory data fusion for process modeling, prognostics, and fault diagnosis. He has led several research projects funded by the National Science Foundation of China, the Shanghai Sailing Program, and other initiatives. WANG has also contributed to numerous publications in top-tier journals and conferences, and he teaches courses on Big Data Analysis and Quality and Reliability Engineering.

Di Wang is a researcher at Shanghai Jiao Tong University specializing in high-dimensional data analysis, time series analysis, and machine learning. Their work focuses on developing advanced statistical methods for modeling complex high-dimensional time series data, including vector autoregressive models, tensor decomposition techniques, and robust estimation approaches. Wang's research also explores nonparametric quantile regression, dynamic spatial autoregression, and matrix time series modeling, with applications in fields such as evolutionary biology and econometrics. Their contributions emphasize the integration of machine learning with traditional statistical methods to address challenges in high-dimensional and structured data analysis.