Kshitij Khare
Basic Information
Mailing Address: Telephone Numbers: Internet: Department of Statistics Office: (352) 273-2985 E-mail: [email protected]fl.edu 103 Griffin Floyd Hall FAX: (352) 392-5175 Web: http://www.stat.ufl.edu/˜kdkhare/ University of Florida Gainesville, FL 32611
Education
PhD in Statistics, 2009, Stanford University (Advisor: Persi Diaconis)
Masters in Mathematical Finance, 2009, Stanford University
Masters in Statistics, 2004, Indian Statistical Institute, India
Bachelors in Statistics, 2002, Indian Statistical Institute, India
Academic Appointments
University of Florida: Associate Professor of Statistics, 2015-present
University of Florida: Assistant Professor of Statistics, 2009-2015
Stanford University: Research/Teaching Assistant, Department of Statistics, 2004-2009
Research Interests
High-dimensional covariance/network estimation using graphical models
High-dimensional inference for vector autoregressive models
Markov chain Monte Carlo methods Kshitij Khare 2
Publications
Core Statistics Research
Ghosh, S., Khare, K. and Michailidis, G. (2019). “High dimensional posterior consistency in Bayesian vector autoregressive models”, Journal of the American Statistical Association 114, 735-748.
Khare, K., Oh, S., Rahman, S. and Rajaratnam, B. (2019). A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data, Machine Learning 108, 2061-2086.
Cao, X., Khare, K. and Ghosh, M. (2019). “High-dimensional posterior consistency for hierarchical non- local priors in regression”, Bayesian Analysis 15, 241-262.
Chakraborty, S. and Khare, K. (2019). “Consistent estimation of the spectrum of trace class data augmen- tation algorithms”, Bernoulli 25, 3832-3863.
Cao, X., Khare, K. and Ghosh, M. (2019). “Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models”, Annals of Statistics 47, 319-348.
Qin, Q., Hobert, J. and Khare, K. (2019). “Estimating the spectral gap of a trace-class Markov operator”, Electronic Journal of Statistics 13, 1790-1822.
Zhang, L., Khare, K. and Xing, Z. (2019). “Trace class Markov chains for the Normal-Gamma Bayesian shrinkage model”, Electronic Journal of Statistics 13, 166-207.
Sparks, D., Rajaratnam, B., Khare, K. and Zhang, L. (2018). “Scalable Bayesian shrinkage and uncertainty quantification for high-dimensional regression”, Journal of Computational and Graphical Statistics 28, 174-184.
Khare, K., Rajaratnam, B. and Saha, A. (2018). Bayesian inference for Gaussian graphical models beyond decomposable graphs, Journal of the Royal Statistical Society, Series B 80, 727-747.
Hobert, J.P., Jung, Y.J., Khare, K. and Qin, Q. (2018). Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors, Scandinavian Journal of Statistics 45, 513-533.
Khare, K., Pal, S. and Su, Z. (2017). A Bayesian approach for envelope models, Annals of Statistics 45, 196-222.
Pal, S., Khare, K. and Hobert, J.P. (2017). Trace class Markov chains for Bayesian inference with general- ized double Pareto shrinkage priors, Scandinavian Journal of Statistics 44, 307-323.
Ali, A., Khare, K., Oh, S. and Rajaratnam, B. (2017). Generalized pseudo-likelihood methods for inverse Kshitij Khare 3
covariance estimation, Proceedings of Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida.
Chakraborty, S. and Khare, K. (2017). Convergence properties of Gibbs samplers for Bayesian probit regression with proper priors, Electronic Journal of Statistics 11, 177-210.
Hobert, J.P. and Khare, K. (2016). Discussion of “Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood” by Yang, Wang and He, International Statistical Review 84, 349-356.
Xiang, R., Ghosh, M. and Khare, K. (2016). Consistency of Bayes factors under hyper g-priors with growing model size, Journal of Statistical Planning and Inference 173, 64-86.
Xiang, R., Khare, K. and Ghosh, M. (2015). High dimensional posterior convergence rates for decompos- able graphical models, Electronic Journal of Statistics 9, 2828-2854.
Pal, S., Khare, K., and Hobert, S. (2015). Improving the Data Augmentation algorithm in the two-block setup, Journal of Computational and Graphical Statistics 24, 1114-1133.
Hobert, J. and Khare, K. (2015). Computable upper bounds on the distance to stationarity for Jovanovski and Madrass Gibbs sampler, Annales de la Faculte des Sciences de Toulouse (special Persi Diaconis issue) 24, 935-947.
Khare, K., Oh, S., and Rajaratnam, B. (2015). A convex pseudo-likelihood framework for high dimensional partial correlation estimation, Journal of the Royal Statistical Society B 77, 803-825.
Sparks, D., Khare, K. and Ghosh, M. (2015). Necessary and sufficient conditions for high-dimensional posterior consistency under g-priors, Bayesian Analysis 10, 627-664.
Oh, S., Dalal, O., Khare, K. and Rajaratnam, B. (2014). “Optimization Methods for Sparse Pseudo- Likelihood Graphical Model Selection”, Advacnes in Neural Information Processing Systems 2014, 667-675.
Dasgupta, S., Khare, K., and Ghosh, M. (2014). “Asymptotic expansion of the posterior density in high dimensional generalized linear models”, J. Multivariate Analysis 131, 126-148.
Pal, S. and Khare, K. (2014). Geometric ergodicity for Bayesian shrinkage models, Electronic Journal of Statistics 8, 604-645.
Khare, K. and Hobert, J. P. (2013). Geometric ergodicity of the Bayesian lasso, Electronic Journal of Statistics 7, 2150-2163.
Khare, K. and Mukherjee, N. (2013). Convergence analysis of some multivariate Markov chains using stochastic monotonicity, Annals of Applied Probability 23, 811-833. Kshitij Khare 4
Khare, K. and Hobert, J. P. (2012). Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression, Journal of Multivariate Analysis 112, 108-116.
Khare, K. and Rajaratnam, B. (2012). Sparse matrix decompositions and graph characterizations, Linear Algebra and Its Applications 437, 932-947.
Khare, K. and Hobert, J. P. (2011). A spectral analytic comparison of trace-class data augmentation algo- rithms and their sandwich variants, Annals of Statistics 39, 2585-2606.
Khare, K. and Rajaratnam, B. (2011). Wishart distributions for decomposable covariance graph models, Annals of Statistics 39, 514-555.
Diaconis, P., Khare, K. and Saloff-Coste, L. (2010). Stochastic alternating projections, Illinois Journal of Mathematics 54, 963-979.
Diaconis, P., Khare, K. and Saloff-Coste, L. (2010). Gibbs sampling, conjugate priors and coupling, Sankhya Ser. A 72, 136-169.
Khare, K. and Rajaratnam, B. (2010). Covariance trees and related Wishart distributions, AMS CONM Volume, Algebraic Methods in Statistics and Probability II, Editors M.Viana and H.Wynn.
Khare, K. and Zhou, H. (2009). Rates of convergence of some multivariate Markov chains with polynomial eigenfunctions, Annals of Applied Probability 19, 737-777.
Diaconis, P., Khare, K. and Saloff-Coste, L. (2008). Gibbs sampling, exponential families and orthogonal polynomials (with discussion), Statistical Science 23, 151-178.
Interdisciplinary Research
Vaziri, S., Awan, O., Porche, K., Scott, K., Sacks, P., Dru, A.B., Chakraborty, S., Khare, K., Hoh, B., and Rahman, M. (2019). Reimbursement Patterns for Neurosurgery: Analysis of the NERVES Survey Results from 2011-2016, Clinical Neurology and Neurosurgery.
Martinez, C.A., Khare, K., Rahman, S. and Elzo, M.A. (2018). Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models, Journal of Theoretical Biology 437, 67-78
Karalkar, N.B., Khare, K., Molt, R. and Benner, S.A. (2017). Tautomeric Equilibria of iso-Guanine and Related Purine Analogs, Nucleosides, Nucleotides and Nucleic Acids 36, 256-274.
Martnez, C.A., Khare, K., Rahman, S., and Elzo, M.A. (2017). Gaussian covariance graph models account- ing for correlated marker effects in genome-wide prediction, Journal of Animal Breeding and Genetics 134, 412-421. Kshitij Khare 5
Vaziri, S., Abbatematteo, J.M., Wilson, J.M., Chakraborty, S., Khare, K., Kubilis, P.S., Hoh, D. (2018). Pre- dictive performance of the American College of Surgeons Universal Risk Calculator in neurosurgical patients, Journal of Neurosurgery 128, 942-947.
Martinez, C.A., Khare, K., Banerjee, A. and Elzo, M.A. (2017). Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the joint probability mass function of genotypes, Journal of Theoretical Biology 417, 8-19.
Martinez, C.A., Khare, K., Banerjee, A. and Elzo, M.A. (2017). Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. II: Multivariate spike and slab priors for marker effects and derivation of approximate Bayes and fractional Bayes factors for the complete family of models, Journal of Theoretical Biology 417, 131-141.
Shahani, N., Swarnkar, S., Giovinazzo, V., Morgenweck, J., Bohn, L.M., Scharager-Tapia, C., Pascal, B., Martinez-Acedo, P., Khare, K. and Subramaniam, S. (2016). RasGRP1 promotes amphetaminein- duced motor behavior through a Rhes interaction network (Rhesactome) in the striatum, Science Sig- naling 9, RA111
Martinez, C., Khare, K. and Enzo, M. (2015). On the Bayesness, minimaxity, and admissibility of point estimators of allelic frequencies, Journal of Theoretical Biology 383, 106-115.
Papers under review
Ghosh, S., Khare, K. and Michailidis, G. (2020). “Strong selection consistency of Bayesian vector au- toregressive models based on a pseudo-likelihood approach”, major revision submitted to Annals of Statistics.
Cao, X., Khare, K. and Ghosh, M. (2020). Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors, minor revision submitted to Journal of Multivariate Analysis.
Backlund, G., Hobert, J.P., Jung, Y.J. and Khare, K. (2020). A hybrid scan Gibbs sampler for Bayesian models with latent variables, minor revision submitted to Statistical Science.
Rahman, R., Khare, K., Michailidis, G., Martinez, C. and Carulla, J. (2020). Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data, under revision for Annals of Applied Statistics.
Jalali, P., Khare, K. and Michailidis, G. (2020). A Bayesian approach to joint estimation of multiple Kshitij Khare 6
graphical models, submitted to Biometrika.
Khare, K., Rahman, S. and Rajaratnam, B. (2020). Scalable and non-iterative graphical model estimation, submitted to Biometrika.
Grants
Co-PI, Statistical methodology for analysis and forecasting with large scale temporal data, 2018-2021 National Science Foundation - Division of Mathematical Sciences (CDS&E) PI: George Michailidis (Statistics, UF) Funding Amount for Khare: $100,000
Co-PI, Development of New Approaches for Analysis of MCMC Algorithms to Facilitate Principled Use of MCMC in Practice, 2015-2019 National Science Foundation - Division of Mathematical Sciences (Statistics) PI: James P. Hobert (Statistics, UF) Funding Amount for Khare: $100,000
PI, “Objective Bayesian Model Selection and Estimation in High Dimensional Statistical Models”, 2011- 2015 National Science Foundation - Division of Mathematical Sciences (Statistics) Funding Amount for Khare: $100,892 co-PI, “Substrate rigidity and gene expression: role of nuclear tension”, 2012-2013 National Institutes of Health PI: Tanmay Lele (Chemical Engineering, UF) Funding Amount (for Khare): $15,672
PhD Students
Past PhD Students
Douglas Sparks (PhD: August 2012), joint with Malay Ghosh. Dissertation title: Posterior consistency of Bayesian regression models
Shibasish Dasgupta (PhD: August 2013), joint with Malay Ghosh. Dissertation title: High dimensional inference and variable selection
Subhadip Pal (PhD: August 2015). Dissertation title: Development and analysis of new Markov chain Monte Carlo (MCMC) algorithms Kshitij Khare 7
Abhishek Saha (PhD: August 2016). Dissertation title: Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non- Decomposable
Ruoxuan Xiang (PhD: August 2016), joint with Malay Ghosh. Dissertation title: Consistency of High Dimensional Bayesian Models
Liyuan Zhang (PhD: August 2017). Dissertation title: Trace Class Markov Chains for Bayesian Shrinkage Models
Syed Rahman (PhD: December 2017). Dissertation title: Cholesky-Based Model Selection and Estimation in Graphical Models
Saptarishi Chakraborty (PhD: August 2018). Dissertation title: Theory and Applications of Markov Chain Monte Carlo Techniques
Satyajit Ghosh (PhD: August 2018), joint with George Michailidis. Dissertation title: Bayesian Estimation and Model Selection Consistency of High Dimensional Time Series
Xuan Cao (PhD: August 2018), joint with Malay Ghosh. Dissertation title: Graphical Models, Nonlocal Priors in High- Dimensional Bayesian Analysis
Peyman Jalali (PhD: May 2019), joint with George Michailidis. Dissertation title: Bayesian estimation and model selection for single and multiple graphical models
Zeren Xing (PhD: August 2019), joint with Nikolay Bliznyuk. Dissertation title: Spectral gap estimation of Markov chains in Bayesian shrinkage model and covariance estimation for spatio-temporal data
Current PhD Students
Suman Bhattacharya (expected completion: August 2020)
Srijata Samanta (expected completion: August 2021), joint with George Michailidis
Sourav Mukherjee (expected completion: August 2021), joint with George Michailidis
Nilanjana Chakraborty (expected completion: August 2021), joint with George Michailidis
Jiayuan Zhou (expected completion: August 2022)
Teaching Experience
University of Florida
Introduction to Probability (STA4321), Introduction to Mathematical Statistics (STA4322), Mathematical Statistics I (STA 6326), Mathematical Statistics II (STA 6327), Generalized Linear Models (STA7249), Special topics course on covariance estimation with graphical models (STA7934). Kshitij Khare 8
Stanford University
Qualifying Examination Instructor, Summer 2006, Summer 2007 and Summer 2008. The assignment is to prepare the first year Ph.D. students for their Qualifying Examinations in Probability Theory, Theoretical Statistics and Applied Statistics.
Major Professional Service
Member of IISA Code of Conduct Policy Committee, 2020
Member of Scientific Program Committee, CMStatistics 2020
Member of Scientific Program Committee, International Indian Statistical Association Conference 2020
Member of Local Organizing Committee, Bayes Comp 2020
Member of Student Paper Award Committee, International Indian Statistical Association Conference 2018
Member of Scientific Program Committee, International Indian Statistical Association Conference 2017
Associate Editor, Journal of Statistical Planning and Inference, 2012 - present
Associate Editor, Statistical Analysis and Data Mining, 2018 - present
Reviewer of articles for Journal of American Statistical Association, Annals of Statistics, Biometrika, Proba- bility Theory and Related Fields, Annals of Applied Probability, Journal of Multivariate Analysis, Electronic Communications in Probability, Statistica Sinica, SIAM Journal of Matrix Analysis, Journal of Computa- tional and Graphical Statistics, Journal of Statistical Planning and Inference, Journal of Machine Learning Research
Major College Level Service (University of Florida)
Member of Sabbatical Committee, College of Liberal Arts and Sciences, Fall 2018.
Major Departmental Service (University of Florida)
Chair of Faculty Search Committee, Spring 2016, 2018, 2020.
Chair of the Organizing Committee for Winter Workshop on Semi-parametric and Non-parametric Infer- Kshitij Khare 9 ence, University of Florida, January 2018.
Member of Executive Committee, 2018-2020.
Member of Ph.D. Curriculum Committee, Spring 2019, Fall 2019.
Member of Merit Committee, Fall 2018, 2019.
Member of PhD Curriculum Committee, Fall 2018.
Member of Faculty Search Committee, Spring 2010, 2012, 2013, 2014, 2019.
Member of Organizing Committee for Winter Workshop on Monte Carlo Methods, University of Florida, January 2013.
Member of the Organizing Committee for Winter Workshop on Causal Inference and Graphical Models, University of Florida, January 2012.
Seminars and Presentations
Conference Presentations
“Bayesian inference in high-dimensional vector autoregressive models and mixed frequency regression”, ASA Florida Chapter Conference, University of West Florida, March 2020.
“Sparsity selection in high-dimensional Bayesian vector autoregressive models”, IISA Conference, Decem- ber 2019, Mumbai, India.
“A Bayesian approach for joint estimation of multiple networks”, BNP 12, June 2019, Oxford University, UK.
“A Bayesian approach for joint estimation of multiple networks”, IISA Conference, May 2018, University of Florida, Gainesville.
“A Bayesian approach for envelope models”, IISA Conference, August 2016, Oregon State University, Corvalis.
“Understanding high-dimensional networks for continuous variables using ECL”, HPCC Systems Commu- nity Day, October 2016, Atlanta, Georgia.
“Bayesian inference for Gaussian graphical models beyond decomposable graphs”, IISA Conference, De- cember 2015, University of Pune, India.
“Bayesian inference for Gaussian graphical models beyond decomposable graphs”, ICSTC, December 2015, University of Kerala, India. Kshitij Khare 10
“Bayesian inference for Gaussian graphical models beyond decomposable graphs”, IASC-ARS, December 2015, NUS, Singapore.
“Methods for Robust High Dimensional Graphical Model Selection”, HPCC Systems Community Day, September 2015, Boca Raton, Florida.
“Methods for Robust High Dimensional Graphical Model Selection”, IISA Conference, Riverside, CA, 2014.
“Methods for Robust High Dimensional Graphical Model Selection”, JPSM Conference honoring Malay Ghosh, University of Maryland, May 2014.
“Convergence analysis of some multivariate Markov chains with polynomial eigenfunctions”, MCMSki, Chamonix, France, January 2014.
“Convergence analysis of some multivariate Markov chains with polynomial eigenfunctions”, Workshop on New Directions in Probability, Indian Statistical Institute, Bangalore, India, June 2013.
“Cholesky based estimation in graphical models”, Workshop on Causal Inference and Graphical Models, University of Florida, Gainesville, January 2012.
“Cholesky based estimation in graphical models”, Joint Statistical Meetings, Miami, August 2011.
“Model selection for covariance graph models”, Joint Statistical Meetings, Vancouver, Canada, August 2010.
“Convergence analysis of some multivariate Markov chains with polynomial eigenfunctions”, Workshop on Orthogonal Polynomials in Statistics and Probability, University of Warwick, UK, July 2010.
“Bayesian inference for covariance graph models”, IMS New Researchers’ Conference, Baltimore, July 2009.
Invited Departmental Seminars
“Bayesian inference in high-dimensional vector autoregressive models and mixed frequency regression”, Departmental Seminar, Texas A & M University, February 2020.
“Sparsity selection in high-dimensional Bayesian vector autoregressive models”, Departmental Seminar, Duke University, October 2019.
“A Bayesian approach for joint estimation of multiple networks”, Departmental Seminar, Texas A & M University, February 2019. Kshitij Khare 11
“Bayesian inference for Gaussian graphical models beyond decomposable graphs”, Departmental Seminar, Florida State university, November 2017.
“Bayesian inference for Gaussian graphical models beyond decomposable graphs”, Department Seminar, October 2017, University of California at Santa Barbara.
“Statistical covariance estimation in the era of high-dimensional data”, Statistics Lecture Series, University of Tampa, September 2017.
“Bayesian inference for Gaussian graphical models beyond decomposable graphs”, Department Seminar, March 2017, University of Illinois at Urbana-Champaign.
“Methods for Robust High Dimensional Graphical Model Selection”, Cornell University, Statistics Seminar, April 2014.
“Improving the Data Augmentation algorithm”, Statistics Seminar, Rutgers University, October 2013.
“Convergence analysis of some multivariate Markov chains with polynomial eigenfunctions”, Probability Seminar, Courant Institute of Mathematical Sciences, New York University, March 2013.
“Convergence analysis of some multivariate Markov chains with polynomial eigenfunctions”, Probability Seminar, Columbia University, March 2013.
“Improving the Data Augmentation algorithm”, Statistics Seminar, North Carolina State University, April 2012.
“Cholesky based estimation in graphical models”, Statistics Seminar, Department of Statistics, Florida State University, November 2011.
“Generalized Wishart distributions”. Probability Seminar, Department of Statistics, Stanford University, Mar 2009.
“Inference in Gaussian covariance graph models”. Department of Statistics, University of Michigan, Feb 2009.
“Inference in Gaussian covariance graph models”. Department of Operations Research, MIT, Feb 2009.
“Inference in Gaussian covariance graph models”. Department of ORFE, Princeton University, Feb 2009.
“Inference in Gaussian covariance graph models”. Department of Statistics, University of Florida, Feb 2009.
“Rates of convergence of some classes of Markov chains with polynomial eigenfunctions”. Probability Seminar, Department of Statistics, Stanford University, Mar 2008. Kshitij Khare 12
“From Markov chains to Gaussian priors and back”. Probability Seminar, Department of Statistics, Univer- sity of California at Berkeley, Feb 2008.
“From Markov chains to Gaussian priors and back”. Department of Statistics, Harvard University, Feb 2008.
“From Markov chains to Gaussian priors and back”. Department of Statistics, Wharton School of Business, Jan 2008.
April 2020