Bio. I am a
fourth-year Ph.D. student in Statistics at Stanford
Duchi. My research interests lie in a convex combination of machine
learning, statstics and optimization, and especially in how to quantify
models uncertainty and make them more robust to changing environments.
Previously, I completed my undergraduate studies at
Polytechnique from which I obtained a B.S. and a
M.S. in 2016 and 2017. I also spent internships at Bloomberg LP
as a Quantitative Researcher in 2017, where I worked with
and Julien Guyon,
and at Google as a Data Scientist in 2019.
- Robust Validation: Confident Predictions
Even When Distributions Shift
- Maxime Cauchois, Suyash Gupta, Alnur Ali, John Duchi.
- In Submission
- Knowing what you know: valid confidence sets in
multiclass and multilabel prediction.
- Maxime Cauchois, Suyash Gupta, John Duchi.
- Accepted to the Journal of Machine Learning Research, March 2021
STATS 100: Mathematics of Sports (Fall 2019).
STATS 361: Causal Inference (Spring 2020).
EE 364A: Convex Optimization (Winter 2020).
STATS 322: Gaussian estimation: Sequence and wavelet models (Fall 2019).
STATS 310A: Theory of Probability I (Fall 2018).
STATS 101: Introduction to Data Science (Summer 2018).
STATS 116: Introduction to Probability (Fall 2017).