Aleksander Mądry I am a Professor of Computer Science in the MIT EECS Department, a member of CSAIL and of the Theory of Computation group. I received my Ph.D. from MIT in 2011. Prior to joining the MIT's faculty, I spent a year as a postdoctoral researcher at Microsoft Research New England and then I was on the faculty of EPFL until early 2015.
Interested in working with me? Apply to our PhD program! (Please do not email me regarding this matter - just mention my name in your application.) Also, please do not contact me regarding internship positions (unless you are an MIT student). |
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Selected Papers (Show all):
- Do Adversarially Robust ImageNet Models Transfer Better?,
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Mądry.
NeurIPS 2020. Oral presentation. - On Adaptive Attacks to Adversarial Example Defenses,
Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Mądry.
NeurIPS 2020. - Circulation Control for Faster Minimum Cost Flow in Unit-Capacity Graphs,
Kyriakos Axiotis, Aleksander Mądry, Adrian Vladu.
FOCS 2020. - From ImageNet to Image Classification: Contextualizing Progress on Benchmarks,
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Mądry.
ICML 2020. - Identifying Statistical Bias in Dataset Replication,
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Mądry.
ICML 2020. - Implementation Matters in Deep RL: A Case Study on PPO and TRPO,
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Mądry.
ICLR 2020. Oral presentation. - A Closer Look at Deep Policy Gradients,
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Mądry.
ICLR 2020. Oral presentation. - Adversarial Examples Are Not Bugs, They Are Features,
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Mądry.
NeurIPS 2019. Spotlight presentation. - Image Synthesis with a Single (Robust) Classifier,
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Mądry.
NeurIPS 2019. - Exploring the Landscape of Spatial Robustness,
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Mądry.
ICML 2019. - Robustness May Be at Odds with Accuracy,
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry.
ICLR 2019. - Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors,
Andrew Ilyas, Logan Engstrom, Aleksander Mądry.
ICLR 2019. - Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability,
Kai Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, Aleksander Mądry.
ICLR 2019. - How Does Batch Normalization Help Optimization?, Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Mądry.
NeurIPS 2018. Oral presentation. - Adversarially Robust Generalization Requires More Data, Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry.
NeurIPS 2018. Spotlight presentation. - Spectral Signatures in Backdoor Attacks, Brandon Tran, Jerry Li, Aleksander Mądry.
NeurIPS 2018. - A Classification-Based Study of Covariate Shift in GAN Distributions, Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry.
ICML 2018. - On the Limitations of First-Order Approximation in GAN Dynamics, with Jerry Li, John Peebles, Ludwig Schmidt.
ICML 2018. - Towards Deep Learning Models Resistant to Adversarial Attacks, with Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu.
ICLR 2018. - k-Server via Multiscale Entropic Regularization, Sebastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee, Aleksander Mądry.
STOC 2018. Invited to the special issue. - Round Compression for Parallel Matching Algorithms, Artur Czumaj, Jakub Łącki, Aleksander Mądry, Slobodan Mitrović, Krzysztof Onak, Piotr Sankowski.
STOC 2018. Invited to the special issue. - A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians, Aleksander Mądry, Slobodan Mitrović, Ludwig Schmidt.
AISTATS 2018. - Matrix Scaling and Balancing via Box Constrained Newton’s Method and Interior Point Methods, Michael B. Cohen, Aleksander Mądry, Dimitris Tsipras, Adrian Vladu.
FOCS 2017. - Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in O(m^10/7 log W) Time, Michael B. Cohen, Aleksander Mądry, Piotr Sankowski, Adrian Vladu.
SODA 2017. - Computing Maximum Flow with Augmenting Electrical Flows, Aleksander Mądry.
FOCS 2016. Invited to the special issue. - On the Resiliency of Randomized Routing Against Multiple Edge Failures, Marco Chiesa, Andrei Gurtov, Aleksander Mądry, Slobodan Mitrović, Ilya Nikolaevskiy, Michael Schapira, Scott Shenker.
ICALP 2016. - The Quest for Resilient (Static) Forwarding Tables, with Marco Chiesa, Ilya Nikolaevskiy, Slobodan Mitrović, Aurojit Panda, Andrei Gurtov, Michael Schapira, Scott Shenker.
INFOCOM 2016. - Fast Generation of Random Spanning Trees and the Effective Resistance Metric, Aleksander Mądry, Damian Straszak, Jakub Tarnawski.
SODA 2015. - On the Configuration LP for Maximum Budgeted Allocation, Christos Kalaitzis, Aleksander Mądry, Alantha Newman, Lukáš Poláček, Ola Svensson.
IPCO 2014. Mathematical Programming, Volume 154 Issue 1, 2015. - Navigating Central Path with Electrical Flows: from Flows to Matchings, and Back, Aleksander Mądry.
FOCS 2013. Best Paper Award. Invited to the Journal of the ACM. - Runtime Guarantees for Regression Problems, Hui Han Chin, Aleksander Mądry, Gary L. Miller, Richard Peng.
ITCS 2013. - The Semi-stochastic Ski-rental Problem, Aleksander Mądry, Debmalya Panigrahi.
FSTTCS 2011. - A Polylogarithmic-Competitive Algorithm for the k-Server Problem, Nikhil Bansal, Niv Buchbinder, Aleksander Mądry, Seffi Naor.
FOCS 2011. Best Paper Award. Journal of the ACM, Volume 62 Issue 5, 2015 (invited paper). - From Graphs to Matrices, and Back: New Techniques for Graph Algorithms
My Ph.D. thesis, MIT, EECS Department, 2011. ACM Doctoral Dissertation Award Honorable Mention. George M. Sprowls Award (for best MIT doctoral theses in CS). - Electrical Flows, Laplacian Systems, and Faster Approximation of Maximum Flow in Undirected Graphs , Paul Christiano, Jonathan Kelner, Aleksander Mądry, Daniel Spielman, Shang-Hua Teng.
STOC 2011. Best Paper Award. Invited to the Journal of the ACM. - Fast Approximation Algorithms for Cut-based Problems in Undirected Graphs, Aleksander Mądry.
FOCS 2010. - Faster Approximation Schemes for Fractional Multicommodity Flow Problems via Dynamic Graph Algorithms, Aleksander Mądry.
STOC 2010. - An O(log n/log log n)-approximation Algorithm for the Asymmetric Traveling Salesman Problem, Arash Asadpour, Michel Goemans, Aleksander Mądry, Shayan Oveis Gharan, Amin Saberi,
SODA 2010. Best Paper Award. - Faster Generation of Random Spanning Trees, Jonathan Kelner, Aleksander Mądry.
FOCS 2009. - Maximum Bipartite Flow in Networks with Adaptive Channel Width, Yossi Azar, Aleksander Mądry, Thomas Moscibroda, Debmalya Panigrahi, Aravind Srinivasan.
ICALP 2009. Theoretical Computer Science, vol. 412(24), 2011. Special Issue. - Susceptible Two-Party Quantum Computations, Andreas Jacoby, Maciej Liśkiewicz, Aleksander Mądry.
ICITS 2008. - Geometric Aspects of Online Packet Buffering: An Optimal Randomized Algorithm for Two Buffers, Marcin Bienkowski, Aleksander Mądry.
LATIN 2008. - Data Exchange: On the Complexity of Answering Queries with Inequalities , Aleksander Mądry.
Information Processing Letters, Vol. 94, Issue 6 (June 2005), p. 253 - 257.
Teaching:
- 6.046: Design and Analysis of Algorithms, Fall 2020.
- 6.046: Design and Analysis of Algorithms, Spring 2020.
- 6.S979: Topics in Deployable ML, Fall 2019.
- 6.854: Advanced Algorithms, Fall 2019.
- 6.046: Design and Analysis of Algorithms, Spring 2019.
- 6.854: Advanced Algorithms, Fall 2018.
- 6.883: Science of Deep Learning, Spring 2018.
- 6.046: Design and Analysis of Algorithms, Spring 2018.
- 6.046: Design and Analysis of Algorithms, Spring 2017.
- 6.854: Advanced Algorithms, Fall 2016.
- 6.006: Introduction to Algorithms, Spring 2016.
- 6.S978: Graphs, Linear Algebra, and Optimization, Fall 2015.
- Theoretical Computer Science, Fall 2014.
- Theory of Computation, Spring 2014.
- Theoretical Computer Science, Fall 2013.
- Advanced Theoretical Computer Science, Spring 2013.
- Theory Gems, Fall 2012.