I am currently a Foundations of Data Science (FDS) Postdoctoral associate at Yale University. Prior to this, I completed my PhD at New York University under the supervision of the wonderful Jonathan Niles-Weed. A copy of my thesis can be found here. Before my PhD, I completed my BA and MSc in Applied Mathematics at McGill University, where I worked with Tim Hoheisel and Adam Oberman.

I study statistical and mathematical aspects of data science. My recent work focuses on analyzing and developing methods for large-scale probabilistic inference and generative modeling, often through the lens of optimal transport.

I am on the 2026-2027 academic job market.

Teaching (Fall 2026)

Course page

CPSC 7430 (Topics in flow-based generative modeling and optimal transport)

This course covers the mathematics of modern generative modeling systems through the lens of optimal transport. Topics will include learning optimal transport maps, diffusion models, stochastic interpolants, sampling under tilts, snapshots of unpaired data, among others.

Selected Papers

All publications

  • Trajectory inference via Acceleration Matching

    Dazzini, B., Conforti, G., Durmus, A., and Pooladian, A-A. (Supervisory role)

    arXivPDFBibTeX

  • Blind denoising diffusion models and the blessings of dimensionality

    Kadkhodaie, Z.*, Pooladian, A-A.*, Chewi, S., and Simoncelli, E. (Joint first author)

    Spotlight at FoGEN ICML workshop; 2026

    arXivPDFTalkBibTeX

  • Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space

    Jiang, Y., Chewi, S., and Pooladian, A-A. (Supervisory role)

    Foundations of Computational Mathematics (2025+)

    arXivPDFSlidesCodeBibTeX

  • Entropic estimation of optimal transport maps

    Pooladian, A-A., and Niles-Weed, J.

    Major Revision (Annals of Statistics)

    Best paper award at OTML NeurIPS workshop; 2021

    arXivPDFCodeBibTeX