
Yang Song (宋飏)
Research Scientist at OpenAI.
Incoming Assistant Professor,
Electrical Engineering and Computing + Mathematical Sciences,
California Institute of Technology (Caltech).
Research: As a researcher in machine learning, I am focused on developing scalable methods for modeling, analyzing and generating complex, high-dimensional data. My interest spans multiple areas, including generative modeling, representation learning, probabilistic inference, AI safety, and AI for science. My ultimate goal is to address problems that have wide-ranging significance, develop methods that are both accessible and effective, and build intelligent systems that can improve human lives.
Previously: I received my Ph.D. in Computer Science from Stanford University, advised by Stefano Ermon. I was a research intern at Google Brain, Uber ATG, and Microsoft Research. I obtained my Bachelor’s degree in Mathematics and Physics from Tsinghua University, where I worked with Jun Zhu, Raquel Urtasun, and Richard Zemel.
news
Jan 3, 2023 | Quanta magazine just released an article on diffusion models, featuring some of my early contributions in the field. Read the story here. |
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Sep 30, 2022 | I will be joining the Department of Electrical Engineering (EE) and Department of Computing + Mathematical Sciences (CMS) at California Institute of Technology as an Assistant Professor starting from January 2024. I am currently seeking self-motivated Ph.D. students and postdoctoral fellows to join my research group in Fall 2023. Candidates with backgrounds in CS, EE, mathematics, physics, or statistics are especially encouraged to apply. If you are interested in working with me as a Ph.D. student, please apply to Caltech CMS/EE and mention my name in your applications. If you are interested in a postdoc position, please contact me directly. |
Aug 30, 2022 | We are organizing a workshop on score-based methods at NeurIPS 2022. Check out our website for details! |