معرفی
Kshitij Khare is a Professor in the Department of Statistics at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research focuses on high-dimensional statistical methods, Bayesian computation, and graphical models. He holds a Ph.D. in Statistics from Stanford University (2009), and prior degrees from Indian Statistical Institute (B.Stat. 2002, M.Stat. 2004, and M.Math. Finance 2009). His work spans covariance estimation, Bayesian VAR models, MCMC convergence analysis, and applications in genomics, neurosurgery, and dairy science.
He teaches advanced courses including Introduction to Probability (STA4321/5325), Theoretical Statistics I/II (STA 6326/6327), and specialized topics like covariance estimation. His grants include NSF-funded projects on Bayesian model selection, MCMC algorithm analysis, and high-dimensional temporal data methods. Notable collaborations include work on genomic prediction using Gaussian concentration graph models and Bayesian approaches for mixed-frequency data.
Khare's research emphasizes scalable Bayesian methods, posterior consistency, and algorithmic efficiency. His lab develops novel techniques for estimating complex statistical structures in high-dimensional settings, with applications to real-world problems in biology, finance, and engineering. Current projects explore sparse Cholesky-based covariance estimation and Bayesian shrinkage priors for high-dimensional regression.
