Bio. I am a
third-year Ph.D. student in Computer Science at Stanford
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
Ermon, working on probabilistic models and
reinforcement learning. I completed my undergraduate
Polytechnique from which I obtained a B.S. and a
M.S. in 2014 and 2015. I also spent internships at
Applied Machine Learning in 2016, Google
Brain in 2017 where I worked
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
- Stephan Eismann, Daniel Levy, Rui
Shu, Stefan Barztsch, Stefano Ermon.
- UAI, 2018.
- Generalizing Hamiltonian Monte Carlo with
- Daniel Levy, Matthew D. Hoffman,
- ICLR, 2018.
- 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*,
- UAI, 2017.
- Data Noising as Smoothing in Neural Network
- Ziang Xie, Sida I. Wang, Jiwei Li, Daniel
Levy, Aiming Nie, Dan Jurafsky, Andrew
- ICLR, 2017.
Teaching assistant for EE364A: Convex Optimization
taught by John Duchi
for CS229: Machine
by Andrew Ng
Reviewer: ICML 2021, NeurIPS 2020, ICLR 2020, AAAI 2020, ICML 2019, ICLR 2019, AABI 2018, R2L Workshop (at NeurIPS 2018).