Aram-Alexandre Pooladian
Aram-Alexandre Pooladian is a Foundations of Data Science (FDS) Postdoctoral Fellow at Yale University, beginning in 2025. He received his Ph.D. in Data Science from New York University, where he worked under the supervision of Jonathan Niles-Weed. Prior to that, he earned both his B.A. and M.Sc. in Applied Mathematics at McGill University. His research lies at the intersection of applied mathematics, statistics, and computer science, with a particular focus on developing tractable and principled methodologies for large-scale probabilistic inference using the theory of optimal transport.