World's Best Scientists 2026 revealed!
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2025
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Computer Science
USA
2026

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Best Female Scientists

D-Index
143
Citations
96772
World Ranking
202
National Ranking
127

Computer Science

D-Index
143
Citations
97365
World Ranking
53
National Ranking
30

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Best Female Scientists Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2019 - ACM Fellow For contributions to security and privacy
  • 2010 - Fellow of the MacArthur Foundation
  • 2010 - Fellow of John Simon Guggenheim Memorial Foundation
  • 2007 - Fellow of Alfred P. Sloan Foundation

Overview

Dawn Song is affiliated with the University of California, Berkeley, specializing in computer science with a focus on artificial intelligence and information systems. Their research spans a variety of subfields, including computer vision and pattern recognition, signal processing, and intersections with sociology and political science.

Their scholarly output includes over 290 publications primarily in the field of computer science. Within this domain, they have concentrated on topics such as privacy-preserving technologies in data, adversarial robustness in machine learning, and topic modeling. Additional research themes cover advanced malware detection techniques, blockchain technology applications and security, natural language processing techniques, and broader areas in privacy, security, and data protection.

Dawn Song has contributed to numerous frequent publication venues, particularly:

  • arXiv (Cornell University)
  • IEEE Security & Privacy
  • Proceedings on Privacy Enhancing Technologies
  • Science
  • Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security

Frequently collaborating with other researchers, Song's common coauthors include Dan Hendrycks, Chenguang Wang, Vivek Nair, Jacob Steinhardt, and Chulin Xie.

Recent notable papers include:

  • "Advances and Open Problems in Federated Learning" (2020), published in Foundations and Trends® in Machine Learning
  • "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization" (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Extracting Training Data from Large Language Models" (2020), available on arXiv (Cornell University)
  • "Measuring Mathematical Problem Solving With the MATH Dataset" (2021), available on arXiv (Cornell University)
  • "Measuring Massive Multitask Language Understanding" (2020), available on arXiv (Cornell University)

Dawn Song's work has been recognized through several fellowships, including:

  • ACM Fellow (2019), for contributions to security and privacy
  • Fellow of the MacArthur Foundation (2010)
  • Fellow of John Simon Guggenheim Memorial Foundation (2010)
  • Fellow of Alfred P. Sloan Foundation (2007)

Best Publications

  • Practical techniques for searches on encrypted data

    Dawn Xiaoding Song;D. Wagner;A. Perrig

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Random key predistribution schemes for sensor networks

    Haowen Chan;A. Perrig;D. Song

  • Provable Data Possession at Untrusted Stores.

    Giuseppe Ateniese;Randal C. Burns;Reza Curtmola;Joseph Herring

  • Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software

    James Newsome;Dawn Xiaodong Song

  • The Sybil attack in sensor networks: analysis & defenses

    James Newsome;Elaine Shi;Dawn Song;Adrian Perrig

  • Android permissions demystified

    Adrienne Porter Felt;Erika Chin;Steve Hanna;Dawn Song

  • Robust Physical-World Attacks on Deep Learning Visual Classification

    Kevin Eykholt;Ivan Evtimov;Earlence Fernandes;Bo Li

  • On Scaling Decentralized Blockchains

    Kyle Croman;Christian Decker;Ittay Eyal;Adem Efe Gencer

  • Efficient authentication and signing of multicast streams over lossy channels

    A. Perrig;R. Canetti;J.D. Tygar;Dawn Song

  • Advanced and authenticated marking schemes for IP traceback

    Dawn Xiaodong Song;A. Perrig

  • The TESLA Broadcast Authentication Protocol

    Adrian Perrig;Ran Canetti;J. D. Tygar;Dawn Song

  • Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

    Xinyun Chen;Chang Liu;Bo Li;Kimberly Lu

  • Polygraph: automatically generating signatures for polymorphic worms

    J. Newsome;B. Karp;D. Song

  • Model-Contrastive Federated Learning

    Qinbin Li;Bingsheng He;Dawn Song

  • Delving into Transferable Adversarial Examples and Black-box Attacks

    Yanpei Liu;Xinyun Chen;Chang Liu;Dawn Song

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • SIA: secure information aggregation in sensor networks

    Bartosz Przydatek;Dawn Song;Adrian Perrig

  • Semantics-aware malware detection

    M. Christodorescu;S. Jha;S.A. Seshia;D. Song

  • Panorama: capturing system-wide information flow for malware detection and analysis

    Heng Yin;Dawn Song;Manuel Egele;Christopher Kruegel

  • BitBlaze: A New Approach to Computer Security via Binary Analysis

    Dawn Song;David Brumley;Heng Yin;Juan Caballero

Frequent Co-Authors

Elaine Shi
Elaine Shi Carnegie Mellon University
Adrian Perrig
Adrian Perrig ETH Zurich
David Brumley
David Brumley Carnegie Mellon University
Juan Caballero
Juan Caballero Madrid Institute for Advanced Studies
Heng Yin
Heng Yin University of California, Riverside
Prateek Saxena
Prateek Saxena National University of Singapore
Michael K. Reiter
Michael K. Reiter Duke University
Neil Zhenqiang Gong
Neil Zhenqiang Gong Duke University
Prateek Mittal
Prateek Mittal Princeton University
Krste Asanovic
Krste Asanovic University of California, Berkeley

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