I am currently a Research Fellow at UQSciML group, Univ of Michigan, Ann Arbor.
I recently completed my PhD. at NUS IORA, researching on Bayesian inference for intractable likelihoods.
My research focuses on harnessing the power of simulation to avoid the computation of likelihood in high dimensional settings for obtaining Bayesian posterior inferences. I also work in optimal design of experiments, uncertainty quantification and applications of ML and Bayesian Statistics to epidemiology, biological process, healthcare, financial modeling and other complex settings. One important aspect of my research is robust Bayesian inferences and sensitivity of inferences to model misspecifications.
Goal oriented optimal experimental design: directly targetting the goals of the experimenter in place of conventional notions of parameter uncertainty
Investigating the sensitivity of optimal designs under various settings like model misspecification to outliers, priors, etc
Application of Bayesian framework and design of experiments to drug doses for cancer cells
History matching and probabilistic calibration for expensive simulators in climate models