Xiao Wang

Xiao Wang

Xiao Wang; Associate Professor, Statistics (University of Michigan; Statistics; 2005)

Dr.Xiao Wang is known for working in both statistical theory and application, and has a strong background in the applied sciences (e.g. engineering and astronomy).  His education began with a Bachelor and Master of Science concentrating in Mathematics from the University of Science and Technology of China.  He then continued with a PhD in Statistics from the University of Michigan. 

His fascination with statistics centered around its focus on uncertainty and how to convert data into knowledge.  Under the advisement of Professors Vijay Nair and Michael Woodroofe, he concentrated his thesis on two components: estimating dark matter distributions in astronomy and degradation modeling in engineering. 

Upon joining Purdue Statistics, Dr. Wang has benefitted from the large department with many faculty members having diverse research interests.  Because of the size of Purdue Statistics and being a large Big Ten school, he has had various collaboration opportunities with others outside of the department.

One such opportunity brought him to SAMSI for a year program called Challenges in Computational Neuroscience (CCNS).  The CCNS program will address the underlying methodological, theoretical, and computational challenges as applied to the field of neuroscience.  Probability and statistics, dynamical systems, geometry, and computer science will be combined with respect to theory as well as in applications. 

Dr. Wang enjoys interdisciplinary work and collaborating with scientists in different fields. He has a balance of theoretical and applied skills, as well as experience to sustain his interdisciplinary collaborations, and is known for his unique ability to marry theory and application for the purpose of providing proper insight into the true nature of a problem.  In the past, he has been involved in the following collaborative projects with Professor Jinglai Shen, an applied mathematician from the University of Maryland:

  • Estimation of Nonlinear Components and Disturbances in Dynamical Systems with Applications to Threat Detection: Aiming to develop efficient statistical algorithms for the estimation of complex dynamics, nonlinear components and disturbances, with applications to threat detection in engineering and biological systems. (Funded by the NSF)
  • A Constrained Optimal Control Approach to Nonparametric Estimation with Applications to Biological, Biomedical, and Engineering Systems: Aiming to develop statistical theory and algorithms for estimation of shape restricted functions with applications to biological, biomedical, and engineering systems. (Funded by the NSF).

Dr. Xiao Wang’s research program is built upon nonparametric and semiparametric inference, functional data analysis and big data analysis.  He welcomes new Purdue collaborations and can be reached via email: wangxiao@purdue.edu.