PeopleofSuperintelligence

Researcher

Janni Yuval

Janni Yuval is a climate scientist who builds hybrid models that pair machine learning with the physics of the atmosphere. After a B.Sc. in physics at Tel Aviv University in 2010 and an M.Sc. at the Weizmann Institute of Science in 2013, he completed a PhD on storm tracks at Weizmann in 2017. At MIT he was a Lorenz Fellow in Paul O'Gorman's group, and a 2020 Nature Communications paper by the two used a random forest to learn a parameterization of small-scale atmospheric processes that gave stable coarse-resolution climate simulations. He coauthored ClimSim, a hybrid physics-ML climate dataset that received a NeurIPS 2023 Outstanding Paper award, and was one of six equal contributors to NeuralGCM, published in Nature in 2024. He is a Research Scientist at Google Research, where a January 2026 blog post described training NeuralGCM on NASA satellite precipitation observations.

Badges

Timeline

  1. 2010–2017

    Physics and a Weizmann PhD

    He earns a B.Sc. in physics at Tel Aviv University in 2010 and an M.Sc. in physics at the Weizmann Institute of Science in 2013. In 2017, he completes a PhD in the Department of Earth and Planetary Sciences at Weizmann, where he is a student in Yohai Kaspi's group from 2013 and studies storm tracks.

  2. By 2020

    Lorenz Fellow at MIT

    By 2020, he is a Lorenz Fellow, a postdoctoral scholar in the Department of Earth, Atmospheric and Planetary Sciences at MIT, working in Paul O'Gorman's group. His interest turns to integrating machine learning and causal inference methods into climate science, with machine-learned convection parametrization schemes as one example.

  3. 2020

    A stable random-forest parameterization

    In 2020, he and Paul O'Gorman publish a Nature Communications paper that uses a random forest to learn a parameterization from coarse-grained output of a three-dimensional high-resolution idealized atmospheric model. The parameterization gives stable simulations at coarse resolution that replicate the climate of the high-resolution simulation.

  4. 2023

    The ClimSim climate dataset

    In 2023, he is one of the coauthors of ClimSim, a dataset of 5.7 billion pairs of multivariate input and output vectors from multi-scale climate simulations, built for hybrid ML-physics research by a consortium of climate scientists and ML researchers. The paper receives an Outstanding Paper award at NeurIPS 2023.

  5. 2024

    NeuralGCM in Nature

    Nature publishes "Neural general circulation models for weather and climate" in 2024, and he is one of six authors who contributed equally. NeuralGCM combines a differentiable solver for atmospheric dynamics with machine-learning components, and it is competitive with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts.

  6. January 2026

    NeuralGCM trained on satellite rainfall

    In January 2026, he writes a Google Research blog post about a Science Advances paper in which NeuralGCM is trained on NASA satellite-based precipitation observations from 2001 to 2018. The model reproduces average precipitation, precipitation extremes and the daily weather cycle more accurately.

  7. As of October 2026

    Scientist at Google Research

    He is a Research Scientist at Google Research. NeuralGCM, the model he works on there, combines physics-based modeling with a neural network trained on NASA precipitation observations.

Career

  1. NowGoogle ResearchResearch Scientist
  2. Massachusetts Institute of TechnologyLorenz Fellow (postdoctoral scholar)

Education

  1. 2017Weizmann Institute of SciencePhD
  2. 2013Weizmann Institute of ScienceM.Sc. in Physics
  3. 2010Tel Aviv UniversityB.Sc. in Physics

From the biography.

  1. 16% of AI career moves in the 2020s went from a university to a companyIncludes Janni Yuval. We estimate that 16% of AI career moves in the 2020s went from a university to a company; 29% of moves starting at a university went to a company, and 76% ended the university role.
  2. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. Google has the highest combined h-index among AI organizationsIncludes Google. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Sources and credits

Photo: We have no published photo of Janni Yuval yet. Send us a free-licensed photo.

  1. Janni Yuval, EAPS Distinguished Postdoctoral Fellow paocweb.mit.edu. Checked 21 October 2026.
  2. Group Members | Yohai Kaspi weizmann.ac.il. Checked 21 October 2026.
  3. Lorenz-Houghton Postdoctoral Fellowship — Lorenz Center lorenz.mit.edu. Checked 21 October 2026.
  4. Homepage of Paul O'Gorman pog.mit.edu. Checked 21 October 2026.
  5. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  6. NeurIPS 2023 Awards neurips.cc. Checked 21 October 2026.
  7. ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation proceedings.neurips.cc. Checked 21 October 2026.
  8. Neural general circulation models for weather and climate | Nature nature.com. Checked 21 October 2026.
  9. Neural general circulation models for weather and climate research.google. Checked 21 October 2026.
  10. NeuralGCM harnesses AI to better simulate long-range global precipitation research.google. Checked 21 October 2026.