CHEN, Kani

Chen Kani is a renowned statistician with a distinguished academic and research career. He has made significant contributions to the fields of survival analysis, nonparametric statistics, and statistical modeling. His work often focuses on the development and analysis of statistical methods for handling censored data, which is a common issue in survival analysis. Chen has published extensively in top-tier statistical journals, including the *Journal of the American Statistical Association*, *Biometrika*, and *The Annals of Statistics*. His research has also touched on topics such as the bootstrap method, case-cohort sampling, and the analysis of clustered data. In addition to his academic work, Chen has authored books and contributed to educational materials, such as the book *读懂RWA* (Understanding RWA), which is aimed at providing insights into risk-weighted assets in financial contexts. His career has spanned several decades, and he has been affiliated with various academic institutions, where he has mentored numerous students and researchers.

Kani Chen, affiliated with the Hong Kong University of Science and Technology, focuses on Data Science and FinTech. Their research spans statistical methods for survival analysis, including semiparametric transformation models and nonparametric proportional hazards models. Chen also explores adaptive learning strategies using deep reinforcement learning and Q-learning, emphasizing recommendation systems. Additionally, they investigate econometric models for response-biased sampling and causal inference in time-to-event studies. Their work includes analysis of clustered data and robust estimation techniques such as least absolute deviation and product-limit estimators. These contributions highlight a strong emphasis on statistical modeling and its applications in finance and data science.