Aleksander Madry

Aleksander Madry is a Polish-born computer scientist known for contributions to theoretical computer science, algorithmic game theory, machine learning, and cryptography. He is a professor at the Massachusetts Institute of Technology and has held a research position at Google Research. His work spans robust algorithms, optimization, and provable guarantees for adversarial settings, influencing research across computer security, artificial intelligence, and complexity theory.

Early life and education

Madry was born in Poland and completed undergraduate studies at the University of Warsaw, a prominent institution associated with figures like Stefan Banach and the Polish School of Mathematics. He moved to the United States for graduate studies and earned a Ph.D. from the Massachusetts Institute of Technology, joining a lineage of students from labs linked to MIT Computer Science and Artificial Intelligence Laboratory and advisors who collaborated with researchers at Carnegie Mellon University and Stanford University. During his doctoral work he interacted with scholars from Harvard University, Princeton University, and ETH Zurich, situating him within international networks including conferences such as STOC and FOCS.

Academic career and positions

Madry joined the MIT faculty after postdoctoral and visiting appointments that connected him to research groups at Microsoft Research, Amazon Research, and Google Research. At MIT he became part of departments and centers collaborating with faculty from Columbia University, University of California, Berkeley, and Yale University. He has supervised students who went on to positions at institutions including University of Pennsylvania and New York University, and has served on program committees for venues such as NeurIPS, ICML, SODA, and CRYPTO. Madry has also participated in policy and industry forums with representatives from National Science Foundation, Defense Advanced Research Projects Agency, and European Research Council.

Research contributions and notable results

Madry's research developed theoretical foundations for robustness in deep learning, adversarial examples, and certified defenses, building on work by researchers at OpenAI, DeepMind, and Google Brain. He produced influential algorithms connecting convex optimization and spectral graph theory used by teams at Facebook AI Research and groups at Microsoft Research Redmond. His papers introduced techniques drawing from linear programming, semidefinite programming, and reductions common in complexity theory and approximation algorithms. Collaborations with authors from University of Toronto, University of Oxford, and Tsinghua University yielded bounds and trade-offs later cited by scholars at Princeton University, Caltech, and Cornell University. Madry addressed robustness benchmarks discussed at workshops associated with ICLR and integration with empirical protocols developed by Stanford AI Lab and Berkeley AI Research. His results on adversarial training and certified robustness informed follow-on work at NVIDIA Research and influenced industrial deployments at Amazon Web Services and Microsoft Azure.

Awards and honors

Madry received recognitions from academic and professional bodies, including awards linked to ACM and IEEE. He has been invited to deliver keynote talks at NeurIPS, ICML, and RSA Conference, and selected for fellowships associated with Simons Foundation and grants from National Science Foundation. His work has been highlighted by prizes given at conferences like COLT and named lectures sponsored by Google Research and Microsoft Research Cambridge. Peer citations and invited positions connected him to academies such as Association for Computing Machinery and editorial roles for journals related to Journal of the ACM and SIAM Journal on Computing.

Selected publications and influence

Madry authored influential papers published in proceedings of NeurIPS, ICLR, STOC, and FOCS, and articles appearing in journals associated with IEEE Transactions on Information Theory and Communications of the ACM. Key works address adversarial robustness, optimization frameworks, and hardness results that have been adopted by research groups at IBM Research, Huawei Noah's Ark Lab, and startups spawned from Massachusetts Institute of Technology spinouts. His publications are frequently cited alongside those by Ian Goodfellow, Yoshua Bengio, Geoffrey Hinton, Dimitris Papadimitriou, and Sanjeev Arora, reflecting cross-disciplinary impact spanning machine learning, cryptography, and theoretical computer science.

Category:Polish computer scientists Category:Massachusetts Institute of Technology faculty