Yao Zhang

Profile Picture

Hi, I'm Yao. I will soon be joining the Department of Statistics and Data Science at the National University of Singapore (NUS) as an Assistant Professor. I am looking for students to work on projects in predictive inference, causal inference, and areas of machine learning that benefit from a statistical perspective. I will also have postdoctoral positions available, thanks to the generous support of the NUS Presidential Young Professorship. If you are interested in working with me or exploring potential collaborations, feel free to reach out via the email below.

I am currently a postdoctoral researcher in the Department of Statistics at Stanford University, advised by Prof. Emmanuel Candès. I received my Ph.D. in Mathematics at the University of Cambridge, supervised by Prof. Mihaela van der Schaar. I also collaborated with Prof. Qingyuan Zhao on several projects related to causal inference. Prior to my Ph.D., I worked with Dr. Alpha Lee on battery diagnosis and drug discovery.

My research lies at the interface of statistics, machine learning, and their scientific applications. A central theme of my work is developing assumption-lean methods to analyze complex data and models used for prediction and causal inference. These methods are designed to achieve the practical statistical guarantees required in real-world applications:

  • Uncertainty quantification for black-box models (link).
  • Adaptive sample-splitting for randomization tests (link).
  • Multiple testing for complex randomized experiments (link).
  • Average-case sensitivity analysis for unmeasured confounding (link).

In machine learning, I develop statistical methods to improve the performance of black-box models in prediction tasks and beyond. These methods cover model selection, training, regularization, and calibration:

  • Step-wise selection for time-series models (link).
  • Noise contrastive learning for data embeddings (link).
  • Data-adaptive regularization for causal inference (link1, link2).
  • Level-adaptive calibration for conformal prediction sets (link).

In addition to my primary research areas, I enjoy exploring basic sciences and their application of statistics and machine learning. You can find my interdisciplinary work in physics and chemistry (link1, link2, link3, link4).