

Dr. Anuj Srivastava
Professor, Department of Statistics
Distinguished Research Professor Florida State University
Statistical Shape Analysis and Modeling Group (SSAMG)

Office: 106D OSB, FSU, Tallahassee, FL
Phone: (850) 6448832
Fax: (850) 6445271
Email: anuj at stat.fsu.edu

My Google Scholar Profile can be found
here , and DBLP listing
here
I am a professor in the Department of Statistics and a Distinguished
Research Professor at the Florida State University.
Here is my brief intro. I obtained M.S. and Ph.D. degrees in electrical engineering from Washington University in St. Louis, in the years 1993 and 1996, respectively, both under the guidance of
Prof. Michael I. Miller (now at the Johns Hopkins University). During 199697, I was a visiting research scientist at the Division of Applied Mathematics, Brown University. In Fall 1997, I joined the Department of Statistics at the Florida State University as an assistant professor. During 20032006, I was an associate professor, and starting Fall 2007 I am a professor here at FSU. I was awarded the Developing
Scholar Award
(see a description here) in 2005 and the Graduate Mentor Faulty Award in 2008 and 2015.
I received FSU's Distinguished Research Professor Award in 2014
(see a description of the award here) .
During my graduate studies and postdoctoral stay at Brown University, I got a chance to work closely with and learn from
Prof. Ulf Grenander . Over the last three decades, his development of metric patterntheory has been both profound and powerful. An important aspect of this approach is the broad range of the knowledge base that it uses
algebra, geometry, statistics, computational science, and imaging science.
Grenander’s pattern theory has been a major influence on my research and approach.
My main interest lies in the use of geometry and statistics in advancing inferences related to complex objects.
Advanced data collection techniques are leading to newer and complex datasets than what
statisticians have dealt with in the past. For instance, advanced imaging systems are producing
spatialtemporal observations
of complex scenes at tremendous resolutions. Analyzing such data requires basis tools from geometry and
statistics, with help from computational solutions, to make progress. Geometry helps characterize structures
and statistics contributes in modeling their variability. A prime example of this problem area is brain understanding
where structural and functional roles of different anatomical parts are essential for characterizing normal and
abnormal.
Our approach is to develop representations for objects of interest, by studying their
shapes, textures, appearances, and motions. Using probability models on these representations, learned from
past data, we use Bayesian strategies for deriving inferences from given image data.

