My research to date has focused on Bayesian econometrics and time series analysis, with applications in developing innovative approaches to stochastic volatility and high-dimensional time series models analysis with policy implications. I have papers on high dimensional time series analysis with novel methodologies in obtaining analytical expressions for the likelihood of inverse gamma stochastic volatility models in nonlinear, non-Gaussian state space models.

My current work and future research interests explores alternative machine learning and stochastic volatility hybrid approaches for volatility forecasting, with applications in climate risk and development economics.

I am always open to collaboration, particularly on interdisciplinary projects beyond mathematics and finance, and look forward to connecting with those interested in advancing the field of econometrics together.