
Yang Song (宋飏)
Leading Strategic Explorations team at OpenAI
Incoming Assistant Professor,
Electrical Engineering and Computing + Mathematical Sciences,
California Institute of Technology (Caltech).
Research: My goal is to build powerful AI models capable of understanding, generating and reasoning with high-dimensional data across diverse modalities. I am currently focused on developing transferable techniques to improve generative models, including architecture, optimization, training objectives, and data efficiency. I invented many foundational concepts and techniques in (score-based) diffusion models, for which you can find more in a blog post, a quanta magazine article, or a recent interview.
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.