PeopleofSuperintelligence

Researcher

Renjie Liao

Renjie Liao is a machine learning researcher who works on geometric deep learning, deep generative models and probabilistic inference, with applications in computer vision, self-driving and cardiovascular diagnosis. After a BEng at Beihang University in 2011 and an MPhil at the Chinese University of Hong Kong in 2015, they completed a PhD in computer science at the University of Toronto in 2021 under Richard Zemel and Raquel Urtasun. In 2019 they co-authored Graph Recurrent Attention Networks, a graph generator that scales to 5K nodes, and in 2022 they co-authored Gaussian-Bernoulli RBMs Without Tears with Geoffrey Hinton and David Fleet, among others. They became a Canada CIFAR AI Chair in 2023. At the University of British Columbia, they are an assistant professor in electrical and computer engineering, a post they have held since January 2022, and they are also a faculty member at the Vector Institute.

Timeline

  1. 2011–2015

    Beihang and CUHK degrees

    They earn a BEng in Automation at Beihang University in 2011 and an MPhil in computer science at the Chinese University of Hong Kong in 2015, advised there by Jiaya Jia.

  2. 2015–2021

    PhD under Zemel and Urtasun

    They receive a Connaught International Scholarship for Doctoral Students at the University of Toronto, held from 2015 to 2019, and an RBC Graduate Fellowship for 2019 to 2021. In 2021, they complete a PhD in computer science under Richard Zemel and Raquel Urtasun, and during the PhD they work as a Senior Research Scientist at Uber Advanced Technologies Group.

  3. 2019

    Graph Recurrent Attention Networks

    In 2019, they co-author Graph Recurrent Attention Networks (GRAN) with Richard Zemel, Raquel Urtasun and six other authors. The NeurIPS 2019 paper generates a graph one block of nodes at a time using graph neural networks with attention, and it produces graphs of up to 5K nodes. A Best Paper Award at the ICML Workshop on Tractable Probabilistic Modeling also comes in 2019.

  4. By 2021

    Visiting researcher at Google Brain

    By 2021, they are a Visiting Faculty Researcher at Google Brain, working with Geoffrey Hinton and David Fleet, and they are affiliated with the Vector Institute.

  5. October 2022

    Gaussian-Bernoulli RBMs Without Tears

    In October 2022, they post Gaussian-Bernoulli RBMs Without Tears with Simon Kornblith, Mengye Ren, David Fleet and Geoffrey Hinton. The paper introduces a Gibbs-Langevin sampling algorithm and a modified contrastive divergence algorithm that lets Gaussian-Bernoulli restricted Boltzmann machines generate images starting from noise.

  6. 2023

    A Canada CIFAR AI Chair

    In 2023, they are appointed a Canada CIFAR AI Chair for research on geometric deep learning, deep generative models and probabilistic inference, applied to computer vision, self-driving and reinforcement learning. A paper on multi-level transformers for cardiovascular diagnosis wins Best Paper at the MICCAI Workshop on Machine Learning in Medical Imaging in 2023.

  7. 2025

    Three papers at ICLR and NeurIPS

    In 2025, three deep generative modeling papers appear: Discrete Diffusion on Symmetric Groups as an oral at ICLR 2025, and Neural Merton Jump Diffusion and Discrete Flow Matching for Retrosynthesis at NeurIPS 2025.

  8. Since January 2022

    Assistant professor at UBC

    In January 2022, they join the University of British Columbia as an assistant professor in electrical and computer engineering, with an associate membership in computer science, and they are a faculty member at the Vector Institute.

Career

  1. Since 2022University of British ColumbiaAssistant Professor, Electrical and Computer Engineering
  2. NowVector InstituteFaculty Member
  3. UberSenior Research Scientist
  4. Google BrainVisiting Faculty Researcher

Education

  1. 2021University of TorontoPhD
  2. 2015Chinese University of Hong KongMPhil
  3. 2011Beihang UniversityBEng

From the biography.

  1. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Renjie Liao. 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.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Renjie Liao. 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.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of British Columbia. 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.
  4. Nonprofit roles are 2.3 times as common in AI safetyIncludes Vector Institute. 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.

Colleagues

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

Sources and credits

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

  1. Renjie Liao - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 22 October 2026.
  2. Renjie Liao (University of Toronto homepage) cs.toronto.edu. Checked 22 October 2026.
  3. Renjie Liao – CIFAR cifar.ca. Checked 22 October 2026.
  4. Efficient Graph Generation with Graph Recurrent Attention Networks arxiv.org. Checked 22 October 2026.
  5. Gaussian-Bernoulli RBMs Without Tears arxiv.org. Checked 22 October 2026.
  6. Renjie Liao (homepage) lrjconan.github.io. Checked 22 October 2026.