PeopleofSuperintelligence

Researcher

Mohamed Aziz Bhouri

Mohamed Aziz Bhouri is a researcher in machine learning for physical systems, with work on Bayesian dynamical-systems models and hybrid physics-ML climate emulation. At MIT's Department of Mechanical Engineering, he completed a PhD in 2020 with a thesis on reduced-basis methods for elastodynamics. Working with Paris Perdikaris at the University of Pennsylvania, he posted the GP-NODE framework for Bayesian systems identification in March 2021. As an NSF AGS-PRF fellow in Pierre Gentine's lab at Columbia University, he published a memory-based climate parameterization in Chaos in 2023 and co-authored ClimSim, which NeurIPS 2023 recognized as an outstanding datasets and benchmarks paper. He is a quant at Goldman Sachs.

Badges

Timeline

  1. 2020

    Reduced-basis PhD at MIT

    He completes a PhD at MIT in 2020 with a thesis on a two-step port-reduced reduced-basis component method for time-domain elastodynamic equations, applied to structural health monitoring. In February 2020, he and Anthony T. Patera of MIT's Department of Mechanical Engineering post a two-level parameterized model-order reduction approach for time-domain elastodynamics.

  2. 2021

    GP-NODE at Penn

    In 2021, he works with Paris Perdikaris at the University of Pennsylvania's Department of Mechanical Engineering and Applied Mechanics. Their GP-NODE framework, posted in March 2021, performs Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems, with tests on predator-prey systems, systems biology and a 50-dimensional human motion system.

  3. 2023

    Postdoc with Gentine at Columbia

    An NSF AGS-PRF fellow and postdoctoral researcher in Pierre Gentine's lab at Columbia University, he publishes with Gentine in 2023 a memory-based neural-network parameterization with a differentiable solver. The paper, in the journal Chaos, applies it to the Lorenz '96 model and predicts skillful forecasts over a long horizon of the resolved variables compared to instantaneous parameterizations.

  4. 2023

    A NeurIPS outstanding dataset paper

    ClimSim, a dataset of 5.7 billion pairs of multivariate input and output vectors from multi-scale climate simulations for hybrid ML-physics emulation, lists him among its authors. NeurIPS 2023 recognizes the paper as an Outstanding Datasets and Benchmarks paper in the dataset category.

  5. March 2024

    Joint parameter and parameterization inference

    At Columbia University's Department of Earth and Environmental Engineering and the NSF Center for Learning the Earth with Artificial Intelligence and Physics, he, Yongquan Qu and Pierre Gentine post a framework in March 2024 for the joint estimation of physical parameters and machine-learning parameterizations with uncertainty quantification. The ICLR 2024 Workshop on AI4Differential Equations in Science accepts the paper.

  6. As of October 2026

    Quant at Goldman Sachs

    As of October 2026, he is a quant at Goldman Sachs, with research interests in model order reduction, Bayesian methods, the finite element method, machine learning and dynamical systems.

Career

  1. NowGoldman SachsQuant
  2. From 2023Columbia UniversityPostdoctoral researcher

Education

  1. 2020Massachusetts Institute of TechnologyPhD

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Columbia University. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. Nonprofit roles are 2.3 times as common in AI safetyIncludes Columbia University. We estimate where people focused on safety, alignment and evaluation work. Nonprofit roles appear for 25.1% of safety people with known affiliations, compared with 11.0% in other areas.
  3. Universities had a net outflow of 2,068 moves in AI careersIncludes Columbia University. Our estimates of career flows and median h-index across universities and companies in AI, including the role of joint appointments and earlier academic jobs.
  4. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes Columbia University. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.

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. [2002.11084] A Two-Level Parameterized Model-Order Reduction Approach for Time-Domain Elastodynamics (ar5iv) ar5iv.labs.arxiv.org. Checked 21 October 2026.
  2. [2002.11084] A Two-Level Parameterized Model-Order Reduction Approach for Time-Domain Elastodynamics arxiv.org. Checked 21 October 2026.
  3. [2103.03385] Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data arxiv.org. Checked 21 October 2026.
  4. [2103.03385] Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data (ar5iv) ar5iv.labs.arxiv.org. Checked 21 October 2026.
  5. bhouri0412 (Mohamed Aziz Bhouri) · GitHub github.com. Checked 21 October 2026.
  6. Memory-based parameterization with differentiable solver: Application to Lorenz ’96 | NSF Public Access Repository par.nsf.gov. Checked 21 October 2026.
  7. Announcing the NeurIPS 2023 Paper Awards – NeurIPS Blog blog.neurips.cc. Checked 21 October 2026.
  8. 16 Papers Accepted To NeurIPS 2023 | Department of Computer Science, Columbia University cs.columbia.edu. Checked 21 October 2026.
  9. [2306.08754] ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation arxiv.org. Checked 21 October 2026.
  10. [2403.02215] Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming arxiv.org. Checked 21 October 2026.
  11. Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming arxiv.org. Checked 21 October 2026.
  12. Mohamed Aziz Bhouri - Google Scholar scholar.google.com. Checked 21 October 2026.