Computational Statistics: Multivariate Statistics, Data Analysis, Causal Inference, Graphical Models and Network analysis, Future Selection and dimensionality reduction, Methods for complex data, Extreme Value Theory

Bayesian Statistics: models and simulation algorithms

Decision Sciences: Decision models, Decision under uncertainty, Risk Theory, Game Theory

Data analytics: Big Data Science Models, Deep data analytics, Learning Theory and Models

Applied Statistics: Medical Statistics, Statistical Genetics and Genomics, Biostatistics, Graphical Models, Causal Inference

Fields of application: identification of genetic, epigenetic and environmental factors  of chronic diseases, evaluation of intervention (meditation) at a population level. Details about the projects can be found at this link