Bio. I am a third-year Ph.D. student in Computer Science at Stanford University advised by John Duchi. My research interests are in machine learning, optimization, and privacy. I am currently on the job market, looking for positions where I will use my skillset to contribute to large-scale research initiatives to push the boundary of what is possible in AI.

Previously, I was a M.S. student at Stanford University advised by Stefano Ermon, working on probabilistic models and reinforcement learning. I completed my undergraduate studies at Lycée Louis-Le-Grand and Ecole Polytechnique from which I obtained a B.S. and a M.S. in 2014 and 2015. I also spent internships at Facebook Applied Machine Learning in 2016, Google Brain in 2017 where I worked with Jascha Sohl-Dickstein and Matt Hoffman and Google Research in 2020 where I worked with Ananda Theertha Suresh, Satyen Kale and Mehryar Mohri.


Learning with User-Level Privacy
Daniel Levy*, Ziteng Sun*, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh.
NeurIPS, 2021.
Adapting to Function Difficulty and Growth Conditions in Private Optimization
Hilal Asi*, Daniel Levy*, John C. Duchi.
NeurIPS, 2021.
Distributionally Robust Multilingual Machine Translation
Chunting Zhou*, Daniel Levy*, Marjan Ghazvininejad, Xian Li, Graham Neubig
EMNLP, 2021.
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy*, Yair Carmon*, John C. Duchi, Aaron Sidford.
NeurIPS, 2020.
Necessary and Sufficient Geometries for Gradient Methods
Daniel Levy, John C. Duchi.
NeurIPS, 2019. Selected for oral presentation.
Bayesian Optimization and Attribute Adjustement
Stephan Eismann, Daniel Levy, Rui Shu, Stefan Barztsch, Stefano Ermon.
UAI, 2018.
Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein.
ICLR, 2018.
[pdf] [code]
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Daniel Levy, Stefano Ermon.
AAAI, 2018.
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Stephen Mussman*, Daniel Levy*, Stefano Ermon.
UAI, 2017.
Data Noising as Smoothing in Neural Network Language Models
Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Levy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng.
ICLR, 2017.


Winter 2021 Teaching assistant for EE364A: Convex Optimization taught by John Duchi.
Fall 2016 Teaching assistant for CS229: Machine Learning taught by Andrew Ng and John Duchi.


Reviewer: ICML 2021, NeurIPS 2020, ICLR 2020, AAAI 2020, ICML 2019, ICLR 2019, AABI 2018, R2L Workshop (at NeurIPS 2018).