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Minerva Mukhopadhyay
PhD (Indian Statistical Institute, Kolkata)
Assistant Professor, Department of Mathematics and Statistics
Research Interest
Asymptotic Statistics, Bayesian Variable Selection, Nonparametric Inference.
FB-332 B
IIT Kanpur,
Kanpur 208016
Education
Post-doctoral Associate in Duke University under the supervision of Prof. David Dunson and Prof. Sandeep Dave
PhD in Statistics from Indian Statistical Institute (ISI), 2016., Kolkata under the supervision of Prof. Tapas Samanta
M.Sc. in Statistics, University of Calcutta, 2009 (63.50%).
B.Sc. in Statistics, St. Xavier’s College, University of Calcutta, 2007 (73.25%).
Teaching Area
Assistant Professor at Department of Statistics, Bethune College, Kolkata, India for the Bachelors’ program in Statistics from September, 2014 till October, 2016.
Taught two short courses at a workshop on Business Statistics organized by Interdisciplinary Statistical Research Unit, ISI, Kolkata at ISI, Tezpur on December, 2016.
Taught the graduate course ‘STA 611: Introduction to Mathematical Statistics’ in Fall 2017 at Department of Statistical Science, Duke University, USA.
Awards & Fellowships
Best Student Paper Award in ‘Theory and Methods’ at IISA 2015 Conference.
Pillar Iglesias Travel Award at ISBA 2016 World Meeting.
O’Bayes Travel Award at O’Bayes meeting 2017.
Conference Presentations
Invited speaker in the session on “MCMC Algorithms for High Dimensions” at the conference organized by the IISA, Florida, USA, May, 2018.
Presented a poster at O’Bayes meeting, UT Austin, USA, December, 2017.
Participated in poster presentation at ‘International Society for Bayesian Analysis (ISBA) – World Meeting’, Sardinia, Italy, June 2016.
Participated in ‘Student Paper Competition’ at the conference organized by the International Indian Statistical Association (IISA), Pune, India, December, 2015.
Invited speaker in the session on “High-Dimensional Data” at the International Statistics Conference organized by the Institute of Applied Statistics (IASSL), Colombo, Sri Lanka, December, 2014.
Contributed speaker at the Joint Statistical Meetings (JSM), Boston, USA, August, 2014.
Papers
Published Papers
Biswas, M., Mukhopadhyay, M. and Ghosh, A. K. (2014) A distribution-free two sample run test applicable to high-dimensional data. Biometrika, 101(4), 913-926.Mukhopadhyay, M., Samanta, T., Chakrabarti, A. (2015) On consistency and optimality of Bayesian variable selection based on g-prior in normal linear regression models. Annals of the Institute of Statistical Mathematics, 67(5), 963-997.Biswas, M., Mukhopadhyay, M. and Ghosh, A. K. (2015) On some exact distributionfree one-sample tests for high dimension low sample size data. Statistica Sinica, 25, 1421-1435.Mukhopadhyay, M. and Samanta, T. (2017) A mixture of g-priors for variable selection when the number of regressors grows with the sample size. TEST, 26, 377-404.Papers communicated for publication/working papers
Mukhopadhyay, M., and Dutta, S. (2018+) Bayesian variable selection for ultrahighdimensional sparse linear models. Available at http://arxiv.org/abs/1609.06031Mukhopadhyay, M., and Dunson, D. (2018+) Targeted random projection for prediction from high-dimensional features. Available at https://arxiv.org/abs/1712.02445Mukhopadhyay, M., and Bhattacharya, S. (2018+) Bayesian non-parametric variable selection using Gaussian processes.References
Other Academic Activities
Visiting Scientist at Interdisciplinary Statistical Research Unit, ISI, Kolkata, India from November, 2016 to December, 2016.
Project Personnel in Machine Intelligence Unit, ISI, Kolkata in 2009-2010 under the supervision of Prof. C. A. Murthy.
Journal Reviews
- TEST (An Official Journal of the Spanish Society of Statistics and Operations Research),
- JSPI (Journal of Statistical Planning and Inference),
- Stat (The ISI’s journal for the Rapid Dissemination of Statistical Research).
- CSDA (Computational Statistics and Data Analysis)
Experience in Statistical Computing
Knowledgeable in C, and an experienced user of R and Matlab. Developed programs relevant to my research work.