Bio. I am a
fourthyear Ph.D. student in Statistics at Stanford
University advised
by John
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
Ecole
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
Bruno Dupire
and Julien Guyon,
and at Google as a Data Scientist in 2019.
Publications
Preprints
 Robust Validation: Confident Predictions
Even When Distributions Shift
 Maxime Cauchois, Suyash Gupta, Alnur Ali, John Duchi.
 In Submission

[arxiv]
[pdf]
 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

[arxiv]
[pdf]
Teaching
Instructor
STATS 100: Mathematics of Sports (Fall 2019).
Teaching Assistant
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).