I received my Ph.D. from the Division of Applied Mathematics at Brown University in May, 2010.
My primary research topics include the development of conditional modeling techniques with applications
in finance and computer vision, hierarchical representations and sequential computation schemes in Bayesian image analysis,
model-based hedging strategies of financial derivatives, reproducibility, and disease association studies.
Most of my research projects are motivated by real problems.
Optimization in derivative hedging is an essential problem in financial industry,
allowing market makers to spread out and reduce their risk exposure when initiating a derivative trading market;
Our generative hierarchical models, with grammar-like structures for image interpretations,
are constructed to tackle the vision problem, my favorite artificial intelligence problem,
where the goal is to narrow down the gap between human and machine performances in image
detection and recognition; Cross-study validation is investigated to address the lack of
reproducibility, which is one of the biggest issues in Biomedical sciences. In addition, I am also
interested in disease association studies, on which I currently collaborate
with faculty in Department of Statistics and College of Public Health
at The Ohio State University.