Draft:Aleksander Mądry

Aleksander Madry is a Polish computer science professor at the Massachusetts Institute of Technology (MIT) and a principal investigator at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) who conducts research in the fields of algorithms, machine learning and optimization.

As of 2024 he is on leave at OpenAI leading preparedness team.

Biography
Aleksander Mądry was born in Wrocław, Poland and obtained a bachelor's degree in theoretical physics from the University of Wrocław. He continued his studies at the Massachusetts Institute of Technology with an M.S. in computer science ("Faster Generation of Random Spanning Trees") and a Ph.D. in 2011 [2] under the supervision of Michel Xavier Goemans and Jonathan A. Kelner ("From Graphs to Matrices, and Back: New Techniques for Graph Algorithms"). His dissertation was awarded the Association of Computing Machinery’s Doctoral Dissertation Award Honorable Mention. and MIT’s George M. Sprowls Award for best thesis in computer science

In 2015 he joined the MIT Electrical Engineering and Computer Science Department.

Research
Aleksander Mądry's early work made substantial contributions to the theory of algorithms. In 2011 he developed an approximation algorithm for the maximum flow problem, the first improvement in many years. And then, in 2013, he gave an exact calculation algorithm for the maximum flow problem which was the first to improve on the prior bound of Evan and Tarjan in 1975. Mądry also contributed advances in the k-server problem and the traveling salesman problem. The Pressburger Award wrote: "Aleksander’s results have been celebrated in the community not only because he broke long standing complexity barriers but moreover because he introduced new and very different techniques to the field which since have successfully been picked up by others."

Mądry's most recent work focuses on machine learning and artificial intelligence. In 2018 he developed a method of "adversarial training" to improve the robustness of machine learning models. Since 2018 Mądry has extended his work to cover other areas of explaining why machine learning works, and investigating the role of data in the accuracy of machine learning models.

Mądry has testified to congress on the dangers of the use of machine learning.

Awards

 * Pressburger Award 2018. (https://eatcs.org/index.php/presburger)
 * FOCS 2013. Best Paper Award
 * FOCS 2011. Best Paper Award
 * STOC 2011. Best Paper Award
 * SODA 2011. Best Paper Award