Aleksander Mądry I am an NBX Career Development Associate 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. Research Interests: My research aims to identify and tackle key algorithmic challenges in today's computing. My goal is to develop theoretical ideas and tools that, ultimately, will change the way we approach optimization -- in all shapes and forms, both in theory and in practice. I am also interested in understanding machine learning from a security/robustness perspective and building the science of modern ML. To read about my work in machine learning, see here (or take a look at our blog). Email: madry@mit.edu    My Curriculum Vitae    Twitter 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).

Selected Papers (Show all):

• 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, with Sebastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee.
  STOC 2018. Invited to the special issue.
• Round Compression for Parallel Matching Algorithms, with Artur Czumaj, Jakub Łącki, Slobodan Mitrović, Krzysztof Onak, Piotr Sankowski.
  STOC 2018. Invited to the special issue.
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians, with Slobodan Mitrović, Ludwig Schmidt,
AISTATS 2018.
• Matrix Scaling and Balancing via Box Constrained Newton’s Method and Interior Point Methods, with Michael B. Cohen, Dimitris Tsipras, Adrian Vladu.
  FOCS 2017.
• Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in O(m^10/7 log W) Time, with Michael B. Cohen, Piotr Sankowski, Adrian Vladu.
  SODA 2017.
• Computing Maximum Flow with Augmenting Electrical Flows,
  FOCS 2016. Invited to the special issue.
On the Resiliency of Randomized Routing Against Multiple Edge Failures, with Marco Chiesa, Andrei Gurtov, 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, and Scott Shenker,
INFOCOM 2016.
Fast Generation of Random Spanning Trees and the Effective Resistance Metric, with Damian Straszak, and Jakub Tarnawski,
SODA 2015.
On the Configuration LP for Maximum Budgeted Allocation, with Christos Kalaitzis, Alantha Newman, Lukáš Poláček, and Ola Svensson,
IPCO 2014. Mathematical Programming, Volume 154 Issue 1, 2015.
• Navigating Central Path with Electrical Flows: from Flows to Matchings, and Back,
  FOCS 2013. Best Paper Award. Invited to the Journal of the ACM.
Runtime Guarantees for Regression Problems, with Hui Han Chin, Gary L. Miller, and Richard Peng,
ITCS 2013.
The Semi-stochastic Ski-rental Problem, with Debmalya Panigrahi,
FSTTCS 2011.
• A Polylogarithmic-Competitive Algorithm for the k-Server Problem, with Nikhil Bansal, Niv Buchbinder, and 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 , with Paul Christiano, Jonathan Kelner, Daniel Spielman, and 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,
FOCS 2010.
Faster Approximation Schemes for Fractional Multicommodity Flow Problems via Dynamic Graph Algorithms,
STOC 2010.
• An O(log n/log log n)-approximation Algorithm for the Asymmetric Traveling Salesman Problem, with Arash Asadpour, Michel Goemans, Shayan Oveis Gharan, and Amin Saberi,
  SODA 2010. Best Paper Award.
Faster Generation of Random Spanning Trees, with Jonathan Kelner,
FOCS 2009.
Maximum Bipartite Flow in Networks with Adaptive Channel Width, with Yossi Azar, Thomas Moscibroda, Debmalya Panigrahi and Aravind Srinivasan,
ICALP 2009. Theoretical Computer Science, vol. 412(24), 2011. Special Issue.
Susceptible Two-Party Quantum Computations, with Andreas Jacoby and Maciej Liśkiewicz,
ICITS 2008.
Geometric Aspects of Online Packet Buffering: An Optimal Randomized Algorithm for Two Buffers, with Marcin Bienkowski,
LATIN 2008.
Data Exchange: On the Complexity of Answering Queries with Inequalities
Information Processing Letters, Vol. 94, Issue 6 (June 2005), p. 253 - 257.

     Teaching:

• 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.

     Other:

• I was/am/will be on the PC of: SODA 2013, STOC 2013, SWAT 2014, ESA 2014, FOCS 2014, RANDOM 2015, STOC 2017, SPAA 2017, FOCS 2018.
• I co-founded the Interest Group on Algorithmic Foundations of Information Technology (IGAFIT) and the Highlights of Algorithms conference.
• I am co-chairing the Fall 2017 semester program on Bridging Continuous and Discrete Optimization at the Simons Institute for the Theory of Computing.
• I co-organize the Theory of Computation Colloquium and the Harvard/MIT/MSR Theory Reading Group.
• I co-organized the 1st European Meeting on Algorithmic Challenges of Big Data (ACBD 2014), as well as the Algorithmic Meeting and Algorithmic Frontiers workshops.
  (Videos of the talks from the Algorithmic Frontiers workshop are available here.)
• In the past, I have been organizing the MIT Algorithms and Complexity Seminar.