About me

I am a permanent researcher at IRIF, affiliated with CNRS and Université de Paris.

I received my PhD from MIT Math in 2017, where I benefited from the brilliant advising of Jonathan Kelner and Aleksander Mądry. Afterwards, I did a postdoc at Boston University, where I worked with Alina Ene and Lorenzo Orecchia.

I work on multiple aspects of convex and non-convex optimization. My work so far combined tools from continuous optimization and convex geometry in order to obtain improved algorithms for classical discrete problems.

I am also interested in various aspects of Deep Learning, such as designing improved training methods and understanding how these affect generalization properties.

Interested in working with me? Please apply to our PhD program. Also consider the Parisian Master of Research in Computer Science, an elite research program after which students typically transition to a PhD.

Publications

Projection-Free Bandit Optimization with Privacy Guarantees
Alina Ene, Huy L. Nguyễn, Adrian Vladu (AAAI 2021)

Adaptive Gradient Methods for Constrained Convex Optimization
Alina Ene, Huy L. Nguyễn, Adrian Vladu (AAAI 2021)

Circulation Control for Faster Minimum Cost Flow in Unit-Capacity Graphs
Kyriakos Axiotis, Aleksander Mądry, Adrian Vladu
Symposium on Foundations of Computer Science (FOCS 2020)

Improved Convergence for ℓ and ℓ1 Regression via Iteratively Reweighted Least Squares
Alina Ene, Adrian Vladu
International Conference on Machine Learning (ICML 2019)
[talk video] [slides] [poster] [code]

Submodular Maximization with Matroid and Packing Constraints in Parallel
Alina Ene, Huy L. Nguyễn, Adrian Vladu
ACM SIGACT Symposium on Theory of Computing (STOC 2019)
[slides] [poster]

Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu
International Conference on Learning Representations (ICLR 2018)
Oral presentation at the Principled Approaches to Deep Learning workshop, ICML 2017

Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods
Michael B. Cohen, Aleksander Mądry, Dimitris Tsipras, Adrian Vladu
Symposium on Foundations of Computer Science (FOCS 2017)
[slides]

Multidimensional Binary Search for Contextual Decision-Making
Ilan Lobel, Renato Paes Leme, Adrian Vladu
ACM Conference on Economics and Computation (EC 2017)
Invited to the special issue
Appears in Operations Research
[slides] [poster]

Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs
Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu
ACM SIGACT Symposium on Theory of Computing (STOC 2017)
Invited to the special issue
Appears in Highlights of Algorithms 2018

Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in Õ(m10/7 log W) Time
Michael B. Cohen, Aleksander Mądry, Piotr Sankowski, Adrian Vladu
ACM-SIAM Symposium on Discrete Algorithms (SODA 2017)
Appears in Highlights of Algorithms 2017
[slides]

Tight Bounds for Approximate Carathéodory and Beyond
Vahab S. Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong
International Conference on Machine Learning (ICML 2017)
Oral presentation at the Informs Optimization Society Conference 2016
[slides]

Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More
Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Aaron Sidford, Adrian Vladu
Symposium on Foundations of Computer Science (FOCS 2016)
[slides]

Improved Parallel Algorithms for Spanners and Hopsets
Gary L. Miller, Richard Peng, Adrian Vladu, Shen Chen Xu
ACM symposium on Parallelism in Algorithms and Architectures (SPAA 2015)

How to Elect a Leader Faster than a Tournament
Dan Alistarh, Rati Gelashvili, Adrian Vladu
ACM Symposium on Principles of Distributed Computing (PODC 2015)

Online Ranking for Tournament Graphs
Claire Mathieu, Adrian Vladu
International Workshop on Approximation and Online Algorithms (WAOA 2010)
[slides]

Other works

A Parallel Double Greedy Algorithm for Submodular Maximization
Alina Ene, Huy L. Nguyễn, Adrian Vladu

Phenotypic profiling reveals that Candida albicans opaque cells represent a metabolically specialized cell state compared to default white cells
(by contribution) Iuliana Ene, Matthew Lohse, Adrian Vladu, Joachim Morschhäuser, Alexander Johnson, Richard Bennett
mBIO (2016)