I am currently a Ph.D. candidate in Statistics at the University of Florida (UF), advised by Dr. Leo L. Duan.
My research interests center around Bayesian statistics and optimization. I enjoy developing scalable Bayesian algorithms and designing novel statistical models for combinatorial data. I am particularly skilled in theoretical analysis, including convergence rate control and asymptotic analysis. In addition to my analytical work, I enjoy programming in R, Python, and C++. With over five years of experience, I have developed machine learning projects across a variety of applications.
I am passionate about machine learning, statistical modeling, and quantitative research, with a strong belief in the power of data-driven decision-making. Over the last decade, AI has transformed every aspect of our lives, and financial markets are no exception: trading is rapidly shifting from human intuition to systematic, model-driven strategies powered by vast streams of information that no human could process alone.
I embrace this transformation. To me, quantitative research is about building efficient models that harness data, balance risk and reward, and adapt to fast-moving markets. With a background in mathematics and statistics and research experience in scalable Bayesian methods and deep learning architecture, I am prepared to contribute to innovative strategies in a fast-paced, data-driven environment, and I welcome opportunities to connect about roles in quantitative research and machine learning.
Key Words
- High-dimensional data
- Combinatorial problem
- Posterior consistency
Research Interests
- Bayesian Nonparametrics
- Graph-model Based Clustering
- Bayesian Asymptotic Analysis
- Combinatorial Data Analysis
- (Mixed) Inetger Programming
Education
Ph.D. in Statistics (Ongoing), 2021-2026
University of Florida
B.S. in Mathematics and Applied Mathematics, 2016-2020
University of Science and Technology of China
Work Experience
Quantitative Research Intern, Jun 2024 - Aug 2024
Susquehanna International Group (SIG)