BIMSA > Probability, Applied Statistics and Data Science
Probability, Applied Statistics and Data Science

Research Group

Introduction
In statistics, we proposed the functional mapping theory for complex traits for the first time internationally, and derived a series of statistical models and algorithms for dynamic trait gene localization. The significant innovation is the use of interdisciplinary methods to develop computational models to analyze the genetic regulatory mechanisms of biological growth, development, and evolution. The group has published 3 monographs and 350 SCI papers, with a total impact factor of over 1500.
PI
Yuval Peres

Yuval Peres

Professor

Rongling Wu

Rongling Wu

Professor

Administrator
Andrew Best

Andrew Best

Assistant Professor

Ang Dong

Ang Dong

Assistant Professor

Faculty
Tadahisa Funaki

Tadahisa Funaki

Professor

Konstantin Khanin

Konstantin Khanin

Professor

Zhong Wang

Zhong Wang

Professor

Hao Wu

Hao Wu

Professor

Fan Yang

Fan Yang

Professor

Yuhong Yang

Yuhong Yang

Professor

Yongtao Guan

Yongtao Guan

Associate Professor

Qi Wu

Qi Wu

Associate Professor

Fan Yang

Fan Yang

Associate Professor

Xin Zhao

Xin Zhao

Associate Professor

Shuliang Bai

Shuliang Bai

Assistant Professor

Krzysztof Ciosmak

Krzysztof Ciosmak

Assistant Professor

Jiebo Song

Jiebo Song

Assistant Professor

Dengcheng Yang

Dengcheng Yang

Assistant Professor

Yi Liu

Yi Liu

Engineer

Postdocs
Hao Bai

Hao Bai

Postdoc

Chitradipa Chakraborty

Chitradipa Chakraborty

Postdoc

Sohail Farhangi

Sohail Farhangi

Postdoc

Huiying Gong

Huiying Gong

Postdoc

Anatolii Korneev

Anatolii Korneev

Postdoc

Xin Lai

Xin Lai

Postdoc

David Pechersky

David Pechersky

Postdoc

Shuo Qin

Shuo Qin

Chern Instructor

Alexey Tanashkin

Alexey Tanashkin

Postdoc

Thomas Tulu

Thomas Tulu

Postdoc

Shuang Wu

Shuang Wu

Postdoc

Shuaijie Zhang

Shuaijie Zhang

Postdoc

Zhaoqi Zhang

Zhaoqi Zhang

Postdoc

Hao Zuo

Hao Zuo

Postdoc

Visitors
Senya Shlosman

Senya Shlosman

Visiting Research Fellow