Ghassen Jerfel is a 2nd year PhD student at Duke University advised by Katherine Heller and David Dunson.
His research lies at the intersection of Bayesian statistics, optimization, and machine learning. In particular, he’s interested in designing computationally and statistically efficient algorithms for scalable inference and robust uncertainty quantification. Towards this end, Ghassen’s work blends the continuous-time view of sampling from Bayesian analysis with the discrete optimization-based approaches common in frequentist statistics and machine learning.
Ghassen received his BSE and MSE from Princeton University, and has also spent time at Google Research.