Dr. Anuj Srivastava

Fellow, Inst. of Electrical and Electronic Engineers (IEEE)
Fellow, American Statistical Association (ASA)
Fellow, Intern. Assoc. for Pattern Recognition (IAPR)

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) 644-8832
Fax: (850) 644-5271
Email: anuj at stat.fsu.edu

Link to our newly published book on Functional and Shape Data Analysis

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 1996-97, 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 2003-2006, 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 pattern-theory 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. Grenanderís 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 spatial-temporal 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.

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