Associate Professor of Statistics
Tutorial Fellow at University College
I have a Ph.D. in Operations Research and Financial Engineering from Princeton University (2014). After that, I joined the Yale Institute for Network Science at Yale University. I worked two years as a Postdoctoral Associate in the Electrical Engineering department, and one year as an Associate Research Scientist with a joint appointment as a Lecturer in the Computer Science department at Yale.
My research interests lie at the intersection of applied probability, statistics, and computer science. I am interested in the investigation of fundamental principles to perform scalable inference, learning, and optimization in high-dimensional models, and in the design and analysis of algorithms in machine learning, with applications to graphical models and Monte Carlo methods.
- P. Rebeschini and S. Tatikonda, Accelerated Consensus via Min-Sum Splitting, to appear.
- P. Rebeschini and S. Tatikonda, A new approach to Laplacian solvers and flow problems, to appear.
- P. Rebeschini and A. Karbasi, Fast mixing for discrete point processes, 28th Annual Conference on Learning Theory (COLT) (2015).
- P. Rebeschini and R. van Handel, Can local particle filters beat the curse of dimensionality?, Ann. Appl. Probab. 25, No. 5, 2809–2866 (2015).