PeopleofSuperintelligence

Researcher

Rahul G. Krishnan

Rahul G. Krishnan is a machine-learning researcher who applies deep generative models and causal inference to decisions in medicine. After a computer engineering degree at the University of Toronto and a master's at New York University, he coauthored Deep Kalman Filters in 2015 and completed a PhD at MIT in David Sontag's group in 2020. He was a Senior Researcher at Microsoft Research New England before joining the University of Toronto in 2021 as an assistant professor and Canada CIFAR AI Chair, and he received a Tier II Canada Research Chair in Computational Medicine in 2023. He is Assistant Professor of computer science and of laboratory medicine and pathobiology at the University of Toronto, where his group builds causal foundation models such as CausalPFN, a transformer posted in 2025 that infers causal effects for new observational datasets.

Badges

Timeline

  1. 2008–2013

    Computer engineering at Toronto

    He earns a BASc in computer engineering at the University of Toronto, studying there from 2008 to 2013 on a Pearson International Scholarship. As an undergraduate, he builds factor graphs for clustering and for RNA secondary structure prediction.

  2. 2013–2016

    Deep Kalman Filters at NYU

    He completes an MS in computer science at New York University, studying from 2013 to 2016, and receives NYU's Henning Biermann Award in 2016. In November 2015, he posts Deep Kalman Filters with Uri Shalit and David Sontag, which introduces an algorithm for learning a broad spectrum of Kalman filters and applies it to counterfactual inference on electronic health data from 8,000 patients over 4.5 years.

  3. 2017–2020

    A PhD at MIT

    He begins a PhD at MIT in January 2017, working with David Sontag in the Clinical ML Group, and completes it in 2020 with a thesis on inference and learning in deep generative models with applications to electronic health record data. The AAAI 2017 paper Structured Inference Networks for Nonlinear State Space Models, written with Uri Shalit and Sontag, learns an inference network and the generative model together. In 2018, he coauthors Variational Autoencoders for Collaborative Filtering with Dawen Liang, Matthew D. Hoffman and Tony Jebara.

  4. 2021

    From Microsoft to Toronto

    Coming from a post as Senior Researcher at Microsoft Research New England, where he works on machine learning for oncology and histopathology images, he joins the University of Toronto in September 2021 as Assistant Professor in Laboratory Medicine and Pathobiology at the Temerty Faculty of Medicine, with a joint appointment in Computer Science. In 2021, he is appointed a Canada CIFAR AI Chair at the Vector Institute.

  5. 2023

    A Canada Research Chair

    In 2023, he receives a Tier II Canada Research Chair in Computational Medicine and an Amazon Research Award. His group also receives a CIFAR Catalyst Grant and a University of Toronto Data Science Institute Catalyst Grant in 2023.

  6. April 2024

    The What Now? AI podcast

    In April 2024, he and Beth Coleman launch What Now? AI, a University of Toronto podcast on artificial intelligence and its impact on society, with guests including Gillian Hadfield, Roger Grosse and Nick Frosst.

  7. June 2025

    CausalPFN for causal effects

    In June 2025, he coauthors CausalPFN with Vahid Balazadeh, Hamidreza Kamkari and colleagues. The single transformer, trained once on a large library of simulated data-generating processes, infers causal effects for new observational datasets without task-specific adjustment and gives calibrated uncertainty estimates.

  8. As of October 2026

    Causal foundation models at Toronto

    He is Assistant Professor of Computer Science and of Laboratory Medicine and Pathobiology at the University of Toronto, a Canada CIFAR AI Chair and a faculty member of the Vector Institute. His research builds algorithms for decision making that automate clinically meaningful problems, and his group develops causal foundation models such as CausalPFN, IV-ICL and SurvivalPFN that predict causal effects, bounds on causal effects and survival outcomes through in-context learning.

Career

  1. Since 2021University of TorontoAssistant Professor
  2. Since 2021Vector InstituteCanada CIFAR AI Chair
  3. Until 2021Microsoft ResearchSenior Researcher

Education

  1. 2020Massachusetts Institute of TechnologyPhD
  2. 2016New York UniversityMS in computer science
  3. 2013University of TorontoBASc in computer engineering

From the biography.

  1. 16% of AI career moves in the 2020s went from a university to a companyIncludes Rahul G. Krishnan. 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 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. Rahul G. Krishnan – CIFAR cifar.ca. Checked 22 October 2026.
  2. Rahul G. Krishnan people.csail.mit.edu. Checked 22 October 2026.
  3. Deep Kalman Filters arxiv.org. Checked 22 October 2026.
  4. Structured Inference Networks for Nonlinear State Space Models arxiv.org. Checked 22 October 2026.
  5. LMP expands Artificial Intelligence expertise by welcoming Rahul Krishnan lmp.utoronto.ca. Checked 22 October 2026.
  6. Variational Autoencoders for Collaborative Filtering arxiv.org. Checked 22 October 2026.
  7. Research Grants & Awards — Rahul G. Krishnan | University of Toronto cs.toronto.edu. Checked 22 October 2026.
  8. How will AI change our world? U of T podcast explores technology’s impact on society utoronto.ca. Checked 22 October 2026.
  9. CausalPFN: Amortized Causal Effect Estimation via In-Context Learning arxiv.org. Checked 22 October 2026.
  10. Rahul G. Krishnan - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 22 October 2026.
  11. Rahul G. Krishnan — Machine Learning & Computational Medicine, University of Toronto cs.toronto.edu. Checked 22 October 2026.