Yangjun Ruan

Ph.D. Candidate, Computer Science
Machine Learning Group
University of Toronto & Vector Institute

Email: yjruan [at] cs [dot] toronto [dot] edu

I am a Ph.D. student in Computer Science at University of Toronto, where I am fortunate to be advised by Chris Maddison and Jimmy Ba. Currently, I am also a visiting scholar at Stanford University, hosted by Tatsunori Hashimoto.

Previously, I was a student researcher at Google Research and a research intern at Microsoft Research. In summer 2019, I was a visiting student at UCLA, where I worked with Cho-Jui Hsieh. I obtained my Bachelor degree in Information Engineering from Zhejiang University.


My research focuses on the scaling, evaluation, and alignment of language models and agents, especially as they approach or exceed super-human performance levels.

Collaboration opportunities: I am always open to discussion on research ideas and collaboration. If you are a student at UofT with interests in language models, agents, AI safety or other related topics, please do not hesitate to reach out to me!

Selected Publications [Full List]

* below denotes equal contribution

  1. Observational Scaling Laws and the Predictability of Language Model Performance
    Yangjun Ruan, Chris J Maddison, and Tatsunori Hashimoto
    arXiv preprint arXiv:2405.10938, 2024
  2. Identifying the Risks of LM Agents with an LM-Emulated Sandbox
    Yangjun Ruan*, Honghua Dong*, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, and Tatsunori Hashimoto
    In International Conference on Learning Representations (ICLR), 2024 [Spotlight]
  3. Weighted Ensemble Self-Supervised Learning
    Yangjun Ruan, Saurabh Singh, Warren Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, and Joshua V. Dillon
    In International Conference on Learning Representations (ICLR), 2023
  4. Optimal Representations for Covariate Shift
    Yangjun Ruan*, Yann Dubois*, and Chris J Maddison
    In International Conference on Learning Representations (ICLR), 2022
  5. Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
    Yangjun Ruan*, Karen Ullrich*, Daniel Severo*, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, and Chris J Maddison
    In International Conference on Machine Learning (ICML), 2021 [Long talk]


  • Conference reviewer: NeurIPS (2020-), ICLR (2021-), ICML (2021-)
  • Workshop reviewer: NeurIPS Workshop on DGMs Applications (2021), ICML Workshop on Pretraining (2022)

Selected Awards & Honors

  • Ontario Graduate Scholarship, 2023
  • DiDi Gruduate Student Award, 2021
  • CHU Kochen Scholarship (highest honor at Zhejiang University), 2019.
  • Cross-disciplinary Scholars in Science and Technology (CSST), UCLA, 2019.
  • National Scholarship (top 1.5%), 2017, 2018, 2019.
  • Meritorious Winner, Interdisciplinary Contest in Modeling (ICM), 2018.