معرفی
Indrabati Bhattacharya is an Assistant Professor in the Department of Statistics at Florida State University. Their research focuses on advanced statistical methodologies including dynamic treatment regimes, machine learning applications in healthcare, and Bayesian asymptotics. Bhattacharya’s work emphasizes nonparametric Bayesian approaches for addressing partial compliance in sequential decision-making frameworks and developing robust quantile regression techniques.
Key research areas include:
- Quantile Regression and Shape-Restricted Inference
- Bayesian Nonparametric Methods for Multivariate Analysis
- Optimization of Dynamic Treatment Regimes in Clinical Trials
- Development of Marginal Structural Models for Sequential Treatment Decisions
Recent contributions highlight Bayesian Q-learning algorithms for policy optimization under partial compliance scenarios, as well as innovative Gibbs posterior frameworks for multivariate quantile inference. Bhattacharya has also applied Bayesian techniques to sports analytics, notably exploring the Duckworth-Lewis method in cricket.
Current research trends emphasize integrating machine learning with Bayesian statistical theory to address complex real-world problems in healthcare and decision science. No academic awards or grants are explicitly listed in available materials.
