He is actively involved in academic leadership and service at NYU Abu Dhabi, where he serves as a member of the Faculty Council Steering Committee and of the Steering Committee of the Center for Research Computing. He is also Faculty Coordinator for Technology and Specification Process Automation for Stern at NYU Abu Dhabi.
His research focuses on statistical methods for understanding systems that evolve over time under uncertainty. Much of his work is concerned with Bayesian approaches to dynamic models, where key components of the system are not directly observable and must be inferred from noisy data. A recurring aim of his research is to develop mathematically tractable models that allow for exact or near-exact inference, reducing reliance on computationally intensive simulation-based methods.
A central class of models in this work includes diffusion processes and hidden Markov models, which are widely used to represent evolving phenomena in areas such as economics, biology, genetics, signal processing, and neuroscience. He also studies infinite-dimensional stochastic models that describe the evolution of probability distributions over time. These models arise naturally in population genetics, where they capture changing frequencies of species or genetic types, and play a key role in Bayesian nonparametric statistics as flexible prior models for temporally structured data.
He is currently Associate Editor of Bayesian Analysis. He served as an elected member of the Board of Directors of the International Society for Bayesian Analysis until 2025 and chaired the Society’s L.J. Savage Award Committee (Theory and Methods), which recognizes outstanding doctoral dissertations in Bayesian econometrics and statistics. He previously coordinated a master’s program in data science and an interdisciplinary honors program integrating economics with mathematics and statistics.
