725 S. Wright St.
Champaign, IL 61820
Fall 2016 – Assistant Professor of Statistics, University of Illinois at Urbana-Champaign, IL.
2011–2016 Graduate Research/Teaching Assistant, University of Michigan, Ann Arbor, MI.
2010–2011 Quantitative Analyst, Nomura Financial Services, Mumbai, India.
High Dimensional Data
I have a broad research interest in methodological, computational and theoretical research in Statistics motivated by substantial applications and interdisciplinary collaborations. Research directions include high dimensional data analysis, model selection, Bayesian computation, large-scale computational models, functional data, and quantile modeling.
PhD, Statistics, University of Michigan, 2016
M.A., Statistics, University of Michigan, 2012
M.S., Statistics w/Distinction, Indian Statistical Institute, Kolkata, 2010
B.S., Statistics w/Honors, Indian Statistical Institute, Kolkata, 2008
Awards and Honors
ProQuest Distinguished Dissertation Award, University of Michigan, 2017
Rackham Predoctoral Fellowship, University of Michigan, 2016
Excellence in Teaching Award, University of Michigan, 2015
Best Student Paper Award, Nonparametric Statistics Section, American Statistical Association, 2015
Best Student Paper Award, Statistical Learning and Data Science Section, American Statistical Association, 2014
Outstanding PhD Student Award, University of Michigan, 2012
Additional Campus Affiliations
Affiliate, Carl R. Woese Institute for Genomic Biology
Narisetty, N. N. (2020). Discussion. International Statistical Review, 88(2), 330-334. https://doi.org/10.1111/insr.12392
Gan, L., Narisetty, N. N., & Liang, F. (2019). Bayesian Regularization for Graphical Models With Unequal Shrinkage. Journal of the American Statistical Association, 114(527), 1218-1231. https://doi.org/10.1080/01621459.2018.1482755
Narisetty, N. N. (Accepted/In press). Bayesian model selection for high-dimensional data. Handbook of Statistics. https://doi.org/10.1016/bs.host.2019.08.001
Narisetty, N. N., Mukherjee, B., Chen, Y. H., Gonzalez, R., & Meeker, J. D. (2019). Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes. Statistics in Medicine, 38(9), 1582-1600. https://doi.org/10.1002/sim.8059
Narisetty, N. N., Shen, J., & He, X. (2019). Skinny Gibbs: A Consistent and Scalable Gibbs Sampler for Model Selection. Journal of the American Statistical Association, 114(527), 1205-1217. https://doi.org/10.1080/01621459.2018.1482754