Anindya Bhadra

Anindya Bhadra

Anindya Bhadra; Assistant Professor, Statistics (University of Michigan; Statistics; 2010)

Dr. Anindya Bhadra received his PhD in statistics from the University of Michigan in 2010.  His PhD dissertation work involved developing computation-intensive Monte Carlo inference procedures for hidden Markov models. Using the techniques he developed, he went on to collaborate with biologists at the University of Michigan to successfully address the role of environmental factors such as rainfall on malaria outbreaks in Northwest India.

After his PhD, Anindya spent two years as a postdoctoral fellow at Texas A&M University. There his methodological interests broadened to include works on joint variable and covariance selection in high-dimensional genomic data. Anindya  joined Purdue Statistics in August of 2012 as an Assistant Professor, because of the strength and diverse research interests of its faculty members.

At Purdue Statistics, Anindya has been actively developing his research program, while forging some new collaboration with both people at Purdue and outside. One such venture is his recent work on a sparse signal recovery prior called the “horseshoe+ prior.” Anindya collaborates with former Purdue Statistics PhD student Jyotishka Datta, current student Yunfan Li, and Prof. Nick Polson at the University of Chicago. This research has led to peer-reviewed publications in leading statistics journals and is currently funded by Grant No. DMS-1613063 by the NSF. Anindya’s other methodological research includes modeling of genomic datasets displaying complex interactions. He is also involved in collaborative work with a team consisting of members of nutrition science, school of nursing and electrical engineering at Purdue with a goal of analyzing temporal patterns in dietary data.

Anindya’s awards and honors include a New Researcher Fellowship from the Statistical and Applied Mathematical Sciences Institute (SAMSI) in 2014; an elected membership of the International Statistical Institute (ISI) in 2015; and an Outstanding Assistant Professor Undergraduate Teaching Award from Purdue Statistics in 2016.

In summary, Dr. Anindya Bhadra’s research program is focused on all aspects of Bayesian methodology for complex and high-dimensional data and on computation-intensive statistical methods. Detailed description of his research can be found on his webpage at He actively welcomes new Purdue collaborations and can be reached via email at: