My principal research focus has been on developing theory, methodology and computational tools to solve emerging problems in biomedicine and science more generally, especially under fully Bayesian settings. Through my statistical training and collaborations, I have developed interests in biostatistics, mixture models, hidden Markov models, causal inference.

Specifically, my statistical methodological research focuses on:

• Continuous-time hidden Markov models;
• Doubly robust causal estimation;
• Robust Bayesian inference via the inverse specification.

• Health trajectories, where this type of data is found in electronic health records, administrative databases and data derived from mobile applications;
• Data-driven engineering design, with focuses on statistical tools for uncertainty quantification and how to apply them into data-driven design. This provides insights on resolving societal challenges in the engineering design;
• Transportation, with focuses on developing appropriate statistical models to solve causal problems.

I am currently the organizer of the statistics seminar at KCL. If you are interested in attending the seminars or have any comments, please email me.