Hao Zhang

 
  • Research
  • Research Profile
  • Papers
  • Grants
  • Teaching
  • STAT 598 R for Data Science (Fall 2018)

  • STAT/FNR 598 Modern Applied Statistics (Spring 2015)

  • Applied Spatial Statistics (Spring 2014)

  • STAT 598 Spatio-temporal Extremes and Point Processes (Fall 2013)

  • STAT/FNR 598 Modern Applied Statistics (Spring 2013)

  • STAT 511 Statistical Methods (Fall 2011)

  • STAT 520 Time Series Analysis and Applications (Spring 2011)

  • STAT 514 Design of Experiments (Fall 09)

  • Analysis of Massive Dependent Data (Fall 08)

  • Useful Links
  • Workshops and Meetings
  • Statistical Software for Spatial Data
  • Statistical Journals

Welcome to my homepage

I am Professor of Statistics and Professor of Forestry and Natural Resources, and have a split appointment with Department of Statistics (75%) and Department of Forestry and Natural Resources (25%). This joint appointment facilitates interdisciplinary research and provides me access to various applications in forestry, agriculture and natural resources. Those applications in turn shape my methodological research in statistics. I strongly believe that methodological research and applications mutually enhance each other.

My current research is primarily in the analysis of spatial and space-time data. These kinds of data are being observed in many fields such as climatology, geophysics, geology, natural resources, agriculture, health sciences, economics and marketing. Technological advances have made it feasible to collect and archive space-time data at large scales that were not possible in just a decade ago. These massive and correlated data create challenging and interesting statistical problems, and demand innovative computational and methodological research.

I employ probabilistic, statistical and computational theory and tools in my research. Since the research problems I deal with range from very applied to highly theoretical ones, my students have the choice of the kind of problems they wish to study. Some has worked on methodology and derived interesting asymptotic results; Some focused on the computational algorithms; and others may focus on specific applications.

 

Department of Statistics, 536 Mathematical Science Building, Purdue University, West Lafayette, IN 47906. Phone: (765)496-9548. Email: zhanghao@purdue.edu