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 a B.Tech degree in Electronics Engineering from IIT-BHU (Varanasi, India) in 1990. During graduate school, I received 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 became a full Professor at FSU. I was recognized as a Distinguished Research Professor by FSU in 2014.

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 four 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. Grenander’s pattern theory has been a major influence on my research and approach.

I wrote the following part in 1998 and it still describes well my research interests!

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. Our approach is to develop representations for objects of interest, by studying their shapes, appearances, and motions. Using probability models on these representations, learned from past data, we use Bayesian strategies for deriving inferences from given image data.