Zheng Zhang is an Associate Professor (with tenure) at the Institute of Statistics and Big Data, Renmin University of China. He earned a Ph.D. in 2015 from the Department of Statistics, Chinese University of Hong Kong. His research fields mainly focus on causal inference and econometrics. Kun Zhang is an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China. He obtained a bachelor degree in Mathematics and Applied Mathematics, master degree in Probability Theory from Beijing Normal University, and doctoral degree in Management Science from City University of Hong Kong. His research interests include stochastic simulation, machine learning, financial engineering and risk management.

Xing Yan is an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China. He obtained his Ph.D. degree from the Chinese University of Hong Kong in 2019. He works at the intersection of AI and finance. He does research on problems in the areas of financial engineering and FinTech such as risk management, asset pricing, quantitative investments, and derivatives, with machine learning and data science methodologies. His research interests also include generative learning, causal learning, OOD generalization, and uncertainty quantification in machine learning. Songshan Yang is an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China.

He obtained a bachelor degree in Statistics from Beijing Normal University, and doctoral degree in Statistics from Pennsylvania State University. His research interests include high dimensional data analysis, statistical optimization, machine learning and applications of statistical models in finance, physiology and psychology. Yuqian Zhang is an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China. He obtained his Ph.D.from the University of California San Diego in 2022 and a bachelor's degree from Wuhan University in 2016. His research focuses on theory and methodology in causal inference, missing data problems, semi-supervised inference, high-dimensional statistics, and machine learning.