Yao Zhang

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I am an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS). I am looking for students and postdocs to work on projects in predictive inference, causal inference, and areas of machine learning that benefit from a statistical perspective. If you are interested in working with me or exploring collaborations, feel free to reach out via the email below.

Previously, I was 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).