PeopleofSuperintelligence

Researcher

Bamdad Hosseini

Bamdad Hosseini is an applied mathematician whose work connects machine learning with Bayesian inverse problems and uncertainty quantification. He studied mechanical engineering at Sharif University of Technology before moving into applied mathematics at Simon Fraser, where his doctoral research focused on Bayesian inverse problems under John Stockie and Nilima Nigam. He joined Caltech as a postdoctoral fellow in 2018, received the NSERC Postdoctoral Fellowship and the Canadian Governor General's Academic Gold Medal in 2018, and joined the University of Washington in 2021. He developed neural-network and Gaussian-process methods for partial differential equations, kernel methods for operator learning, and analysis of generative priors; he also advised doctoral research on transport-based generative models. As of October 2026, he is an Associate Professor and Reeves Family Faculty Fellow in UW's Department of Applied Mathematics.

Works
60
Citations
897
h-index
14

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2011

    Applied mathematics at Simon Fraser

    In 2011, he begins an MSc in Applied and Computational Mathematics at Simon Fraser University after studying mechanical engineering at Sharif University of Technology. His master's thesis uses CLAWPACK to build a three-dimensional model of atmospheric pollutant dispersion.

  2. 2013

    Bayesian inverse problems at SFU

    In 2013, he begins doctoral research in Bayesian inverse problems at Simon Fraser, with John Stockie and Nilima Nigam as supervisors. His thesis work addresses computational and theoretical aspects of the field.

  3. 2018

    Caltech appointments and awards

    In 2018, he joins Caltech as a postdoctoral fellow. He receives an NSERC Postdoctoral Fellowship and the Canadian Governor General's Academic Gold Medal in 2018. At Caltech, he also serves as a von Karman instructor and senior postdoctoral fellow.

  4. 2020

    Neural networks for parametric PDEs

    In 2020, he coauthors Model Reduction and Neural Networks for Parametric PDEs with Kaushik Bhattacharya, Nikola B. Kovachki and Andrew M. Stuart. The framework combines neural networks with model reduction to approximate input-output maps between infinite-dimensional spaces.

  5. 2021

    Gaussian processes for nonlinear PDEs

    In October 2021, he joins the University of Washington's Department of Applied Mathematics as an assistant professor. He and Yifan Chen, Houman Owhadi and Andrew M. Stuart publish a Gaussian-process framework in 2021 for solving nonlinear PDEs and identifying unknown parameters in inverse problems.

  6. 2022

    Bayesian inference and optimal transport

    In 2022, he explains how machine-learning methods can construct data-driven priors within Bayesian inference and explores the methods through optimal transport.

  7. 2023

    Kernel methods for operator learning

    In 2023, he coauthors Kernel Methods are Competitive for Operator Learning with Pau Batlle, Matthieu Darcy and Houman Owhadi. The paper gives a kernel-based framework for learning operators between Banach spaces, with error analysis and numerical comparisons against neural-network methods.

  8. 2025

    A dissertation on generative models

    In 2025, he advises Biraj Pandey's PhD dissertation, Weaving Order From Uncertainty: Design, Analysis, and Application of Transport-based Generative Models.

  9. 2026

    Generative priors and measure transport

    In 2026, he and Ziqi Huang publish Error Analysis of Bayesian Inverse Problems with Generative Priors, which derives error bounds for generative priors in inverse problems. He also coauthors Approximating Measures on Function Spaces: Transport and Truncation with Ricardo Baptista and Alexander W. Hsu, developing approximations for Bayesian inverse problems and generative modeling.

  10. As of October 2026

    Associate professor at UW

    As of October 2026, he is an Associate Professor and Reeves Family Faculty Fellow in the University of Washington's Department of Applied Mathematics. He teaches computational data analysis and complex-systems courses in 2025 and 2026 while researching machine-learning theory and uncertainty quantification.

Career

  1. NowUniversity of WashingtonAssociate Professor and Reeves Family Faculty Fellow
  2. From 2021University of WashingtonAssistant Professor
  3. From 2018California Institute of TechnologyPostdoctoral Fellow
  4. California Institute of TechnologyVon Karman Instructor and Senior Postdoctoral Fellow

Education

  1. From 2013Simon Fraser UniversityPhD
  2. From 2011Simon Fraser UniversityMSc in Applied and Computational Mathematics
  3. Sharif University of Technology

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Bayes' Rule and the Sound of Rain amath.washington.edu. Checked 27 October 2026.
  2. Bamdad Hosseini - Department of Mathematics - Simon Fraser University sfu.ca. Checked 27 October 2026.
  3. Natural Sciences Division Welcomes 12 New Faculty Members artsci.washington.edu. Checked 27 October 2026.
  4. Model Reduction and Neural Networks for Parametric PDEs arxiv.org. Checked 27 October 2026.
  5. Solving and Learning Nonlinear PDEs with Gaussian Processes arxiv.org. Checked 27 October 2026.
  6. Kernel Methods are Competitive for Operator Learning arxiv.org. Checked 27 October 2026.
  7. Graduation 2025 amath.washington.edu. Checked 27 October 2026.
  8. Error Analysis of Bayesian Inverse Problems with Generative Priors arxiv.org. Checked 27 October 2026.
  9. Approximating Measures on Function Spaces: Transport and Truncation arxiv.org. Checked 27 October 2026.
  10. Bamdad Hosseini | Department of Applied Mathematics | University of Washington amath.washington.edu. Checked 27 October 2026.
  11. Bamdad Hosseini bamdadhosseini.org. Checked 27 October 2026.