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Aleksander Madry

Aleksander Madry

D-Index & Metrics

Computer Science

D-Index
51
Citations
10220
World Ranking
5347
National Ranking
2449

Research.com Recognitions

  • 2016 - Fellow of Alfred P. Sloan Foundation

Overview

Aleksander Madry is affiliated with MIT in the United States, specializing primarily in the field of Computer Science. Their research centers on artificial intelligence, with a focus on adversarial robustness in machine learning. Their body of work spans multiple related topics including domain adaptation and few-shot learning, machine learning and data classification, advanced neural network applications, anomaly detection techniques and applications, COVID-19 diagnosis using AI, and advanced malware detection techniques.

The scientist has contributed extensively to scholarly publications, with a predominant presence in the venue arXiv (Cornell University), accounting for 47 of their works. Other publication venues include the Dagstuhl Research Online Publication Server, IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Neuroscience, and the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Among recent papers authored or co-authored by Aleksander Madry are:

  • On Assessing ML Model Robustness: A Methodological Framework (Academic Track), 2025, Dagstuhl Research Online Publication Server
  • Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • On Adaptive Attacks to Adversarial Example Defenses, 2020, arXiv (Cornell University)
  • Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO, 2020, arXiv (Cornell University)
  • Do Adversarially Robust ImageNet Models Transfer Better?, 2020, arXiv (Cornell University)

Frequent collaborators include:

  • Andrew Ilyas
  • Logan Engstrom
  • Hadi Salman
  • Guillaume Leclerc
  • Dimitris Tsipras

Aleksander Madry's primary subfields of study reflect the breadth of interests within artificial intelligence, including computer vision and pattern recognition, radiology, nuclear medicine and imaging, signal processing, and cognitive neuroscience.

The scientist received the award of Fellow of Alfred P. Sloan Foundation in 2016.

Best Publications

  • Robustness May Be at Odds with Accuracy

    Dimitris Tsipras;Shibani Santurkar;Logan Engstrom;Alexander Turner

  • Adversarial Examples Are Not Bugs, They Are Features

    Andrew Ilyas;Shibani Santurkar;Dimitris Tsipras;Logan Engstrom

  • On Evaluating Adversarial Robustness

    Nicholas Carlini;Anish Athalye;Nicolas Papernot;Wieland Brendel

  • On Adaptive Attacks to Adversarial Example Defenses

    Florian Tramer;Nicholas Carlini;Wieland Brendel;Aleksander Madry

  • Adversarially Robust Generalization Requires More Data

    Ludwig Schmidt;Shibani Santurkar;Dimitris Tsipras;Kunal Talwar

  • Spectral Signatures in Backdoor Attacks

    Brandon Tran;Jerry Li;Aleksander Madry

  • Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs

    Paul Christiano;Jonathan A. Kelner;Aleksander Madry;Daniel A. Spielman

  • A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations

    Logan Engstrom;Brandon Tran;Dimitris Tsipras;Ludwig Schmidt

  • Exploring the Landscape of Spatial Robustness

    Logan G. Engstrom;Brandon Tran;Dimitris Tsipras;Ludwig Schmidt

  • Prior convictions: Black-box adversarial attacks with bandits and priors

    Andrew. Ilyas;Logan G. Engstrom;Aleksander Madry

  • Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses.

    Micah Goldblum;Dimitris Tsipras;Chulin Xie;Xinyun Chen

  • Navigating Central Path with Electrical Flows: From Flows to Matchings, and Back

    Aleksander Madry

  • A Polylogarithmic-Competitive Algorithm for the k-Server Problem

    Nikhil Bansal;Niv Buchbinder;Aleksander Madry;Joseph (Seffi) Naor

  • How Does Batch Normalization Help Optimization

    Shibani Santurkar;Dimitris Tsipras;Andrew Ilyas;Aleksander Madry

  • Do Adversarially Robust ImageNet Models Transfer Better

    Hadi Salman;Andrew Ilyas;Logan Engstrom;Ashish Kapoor

  • Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

    Logan Engstrom;Andrew Ilyas;Shibani Santurkar;Dimitris Tsipras

  • Noise or Signal: The Role of Image Backgrounds in Object Recognition

    Kai Yuanqing Xiao;Logan Engstrom;Andrew Ilyas;Aleksander Madry

  • Clean-Label Backdoor Attacks

    Alexander Turner;Dimitris Tsipras;Aleksander Madry

  • Implementation Matters in Deep RL: A Case Study on PPO and TRPO

    Logan Engstrom;Andrew Ilyas;Shibani Santurkar;Dimitris Tsipras

  • Adversarial Robustness as a Prior for Learned Representations

    Logan Engstrom;Andrew Ilyas;Shibani Santurkar;Dimitris Tsipras

  • Image Synthesis with a Single (Robust) Classifier

    Shibani Santurkar;Andrew Ilyas;Dimitris Tsipras;Logan Engstrom

  • Label-Consistent Backdoor Attacks

    Alexander Turner;Dimitris Tsipras;Aleksander Madry

  • An O(log n/log log n)-approximation algorithm for the asymmetric traveling salesman problem

    Arash Asadpour;Michel X. Goemans;Aleksander Madry;Shayan Oveis Gharan

Frequent Co-Authors

Ashish Kapoor
Ashish Kapoor Microsoft (United States)
Nicholas Carlini
Nicholas Carlini Google (United States)
Niv Buchbinder
Niv Buchbinder Tel Aviv University
Gary L. Miller
Gary L. Miller Carnegie Mellon University
Nikhil Bansal
Nikhil Bansal University of Michigan–Ann Arbor
Richard Peng
Richard Peng Carnegie Mellon University
Daniel A. Spielman
Daniel A. Spielman Yale University
Scott Shenker
Scott Shenker University of California, Berkeley

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