PeopleofSuperintelligence

Researcher

Ricardo Baptista

Also known as Ricardo Miguel Baptista

Ricardo Baptista is a researcher who develops generative models for science and engineering using computational measure transport. He earned a BASc in Engineering Science at the University of Toronto and a PhD in Computational Science and Engineering at MIT in 2022, advised by Youssef Marzouk. His coauthored papers include work on nonlinear ensemble filtering in 2019, monotone triangular transport maps in 2020 and conditional sampling through optimal transport in 2023. Before moving to Toronto, he worked as a von Kármán instructor at Caltech and as a postdoctoral scientist at Amazon, and in 2025 he coauthored papers on memorization in diffusion models and on contrastive learning. Since 2025, he is an Assistant Professor in Statistical Sciences at the University of Toronto and a Faculty Affiliate of the Vector Institute.

Badges

Timeline

  1. 2019

    Transport couplings for ensemble filtering

    In 2019, he coauthors a paper with Alessio Spantini and Youssef Marzouk that generalizes the ensemble Kalman filter (EnKF) to nonlinear updates, using transportation of measures and stochastic or deterministic couplings. The method is demonstrated on the Lorenz-96 model in its chaotic regime.

  2. 2020

    Monotone triangular transport maps

    With Youssef Marzouk and Olivier Zahm, he posts a paper in 2020 on representing and learning monotone triangular transport maps; it appears in Foundations of Computational Mathematics in 2023. The paper gives conditions under which the learning problem has no spurious local minima and applies the maps to density estimation and likelihood-free inference.

  3. June 2022

    An MIT doctorate on triangular transport

    He completes a PhD in Computational Science and Engineering at MIT in June 2022, advised by Youssef Marzouk in the Uncertainty Quantification group. His thesis is "Probabilistic modeling and Bayesian inference via triangular transport", and his earlier degree is a BASc in Engineering Science from the University of Toronto.

  4. 2023

    Conditional sampling via optimal transport

    In 2023, he coauthors a paper with Giulio Trigila, Ryan Tsai and others on a non-parametric generative model that iteratively maps reference samples to a target distribution. The model uses block-triangular transport maps that arise from an optimal transport problem with a weighted L2 cost.

  5. By 2025

    Caltech and Amazon Search

    Before he joins the University of Toronto in 2025, he is a von Kármán instructor in Computing + Mathematical Sciences at Caltech, hosted by Andrew Stuart and Houman Owhadi. He is also a postdoctoral scientist at Amazon Search.

  6. 2025

    Diffusion models and contrastive learning

    In January 2025, he coauthors with Agnimitra Dasgupta, Nikola Kovachki, Assad Oberai and Andrew Stuart an analysis of the dynamical mechanism behind memorization in diffusion models and the need for regularization. In May 2025, he coauthors with Andrew Stuart and Son Tran a probabilistic account of multimodal contrastive learning that also gives rise to crossmodal generative models.

  7. Since 2025

    Assistant professor at Toronto

    He starts a new academic position at the University of Toronto in September 2025 as an Assistant Professor in Statistical Sciences, and joins the Vector Institute as a Faculty Affiliate. His work develops structure-revealing generative models for science and engineering using computational measure transport and studies their mathematical properties.

Career

  1. Since 2025University of TorontoAssistant Professor in Statistical Sciences
  2. Since 2025Vector InstituteFaculty Affiliate
  3. Until 2025California Institute of TechnologyVon Kármán instructor
  4. Until 2025AmazonPostdoctoral Scientist

Education

  1. 2022Massachusetts Institute of TechnologyPhD
  2. University of TorontoBASc

From the biography.

  1. 16% of AI career moves in the 2020s went from a university to a companyIncludes Ricardo Baptista. 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. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Toronto. 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.
  3. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Vector Institute. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Toronto. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. [1907.00389] Coupling techniques for nonlinear ensemble filtering arxiv.org. Checked 22 October 2026.
  2. [2009.10303] On the representation and learning of monotone triangular transport maps arxiv.org. Checked 22 October 2026.
  3. Ricardo Baptista ricardobaptista.com. Checked 22 October 2026.
  4. People - MIT UQGroup uqgroup.mit.edu. Checked 22 October 2026.
  5. Ricardo Baptista - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 22 October 2026.
  6. [2307.04102] A generative flow for conditional sampling via optimal transport arxiv.org. Checked 22 October 2026.
  7. [2501.15785] Memorization and Regularization in Generative Diffusion Models arxiv.org. Checked 22 October 2026.
  8. [2505.24134] A Mathematical Perspective On Contrastive Learning arxiv.org. Checked 22 October 2026.