Research

Most of my research work involves the application and development of Bayesian models and Reinforcement Learning. I believe that the generalized linear mixed model is an undervalued tool which has the potential to bridge many gaps in a variety of contexts: Correct specification of design and contrasts is an elegant and efficient route to resolve many practical problems.

 

Bayesian models can be used in tandem with Machine Learning methods such as Reinforcement Learning as well, in any case where optimal decision-making is needed. Inference in the model-based decision-making framework, of course, is causal. My forthcoming book on Causal Inference and Reinforcement Learning outlines the deep mathematical synergy between these two revolutionary fields, which have largely remained in their respective silos with few cross-citations between them. The book is under contract with CRC press, with estimated completion around May 2027.

 

I always enjoy meeting new professional colleagues, and enjoy active collaboration and idea-sharing on research. If any of these concepts are interesting to you, please do not hesitate to reach out.