John Cherian

I’m a fifth year PhD student in statistics at Stanford University, where I’m grateful to be advised by Emmanuel Candès and supported by the John and Fannie Hertz Foundation.

My research is motivated by fundamental questions about the reliability and fairness of black-box models. For example, does a model treat different groups equitably, or can we quantify model uncertainty before taking action on each prediction? When I’m not working on new methods for model-free inference, I apply these ideas to election forecasting as a consultant to The Washington Post.

Before starting my PhD, I spent 3 years at D.E. Shaw Research (“DESRES”) where I worked on improving polarizable force fields for all-atom simulations. I joined DESRES after earning my B.S. in Mathematical and Computational Science and M.S. in Statistics from Stanford University in 2017.

You can reach me at jcherian at stanford dot edu. A copy of my CV is available here.

selected publications

Large language model validity via enhanced conformal prediction methods

John J Cherian, Isaac Gibbs, and Emmanuel J Candès

In Advances in Neural Information Processing Systems, 2024

Bib PDF Code

@inproceedings{cherian2024llm,
  author = {Cherian, John J and Gibbs, Isaac and Cand{\`e}s, Emmanuel J},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Globerson, A. and Mackey, L. and Fan, A. and Zhang, C. and Belgrave, D. and Tomczak, J. and Pacquet, U.},
  publisher = {Curran Associates, Inc.},
  title = {Large language model validity via enhanced conformal prediction methods},
  url = {https://arxiv.org/abs/2406.09714},
  abs = {https://arxiv.org/abs/2406.09714},
  volume = {37},
  year = {2024},
  month = {}
}

Conformal Prediction With Conditional Guarantees

Isaac Gibbs, John J Cherian, and Emmanuel J Candès

Accepted at Journal of the Royal Statistical Society: Series B, 2025

Bib PDF Code

@article{gibbs2023conformal,
  title = {Conformal Prediction With Conditional Guarantees},
  author = {Gibbs, Isaac and Cherian, John J and Cand{\`e}s, Emmanuel J},
  journal = {Accepted at Journal of the Royal Statistical Society: Series B},
  year = {2025},
  url = {https://arxiv.org/abs/2305.12616},
  abs = {https://arxiv.org/abs/2305.12616},
  month = {}
}

Statistical Inference for Fairness Auditing

John J Cherian, and Emmanuel J Candès

Journal of Machine Learning Research, 2024

Bib PDF Code

@article{cherian2024statistical,
  title = {Statistical Inference for Fairness Auditing},
  author = {Cherian, John J and Cand{\`e}s, Emmanuel J},
  journal = {Journal of Machine Learning Research},
  year = {2024},
  volume = {25},
  number = {149},
  pages = {1--49},
  url = {https://www.jmlr.org/papers/v25/23-0739.html},
  abs = {https://www.jmlr.org/papers/v25/23-0739.html},
  month = {}
}

Election modeling in 2024: a conformal inference approach

John J Cherian, Lenny Bronner, and Emmanuel J Candès

Oct 2024

Bib HTML Code Poster

@techreport{cherianbronner2024post,
  author = {Cherian, John J and Bronner, Lenny and Cand{\`e}s, Emmanuel J},
  institution = {The Washington Post},
  title = {Election modeling in 2024: a conformal inference approach},
  year = {2024},
  month = oct,
}