Sujit Ghosh`s broad research interests have been in the area of Bayesian inference and its applications to biomedical and environmental data using modern computational tools such as Markov Chain Monte Carlo (MCMC) methods. Specific areas of expertise include statistical modeling of and associated methods for (spatially and temporally) correlated data; methods for handling data irregularities (e.g., missing data, censored data etc.); combining mechanistic models (e.g. for HIV dynamics, pharmacokinetics/dynamics) with statistical models to characterize biological mechanisms and their variation in the population; methods for analysis of clinical trials and epidemiological studies, and statistical methods associated with the identification of environmental risk factors (e.g., ambient particulate matter, nitrate concentrations etc.).
