World's Best Scientists 2026 revealed!
Florian Tramèr

Florian Tramèr

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
45
Citations
18304
World Ranking
432
National Ranking
5

Computer Science

D-Index
46
Citations
18768
World Ranking
6653
National Ranking
130

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Florian Tramèr is affiliated with ETH Zurich in Switzerland and has a substantial research output primarily focused on computer science. Their work covers a wide spectrum of topics within the field, emphasizing artificial intelligence and privacy-preserving technologies.

The scientist has published extensively in top venues, with a significant number of papers appearing on arXiv (Cornell University), which accounts for 68 publications. Other notable venues include the Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, Foundations and Trends® in Machine Learning, the 2022 IEEE Symposium on Security and Privacy (SP), and the 2022 ACM Conference on Fairness, Accountability, and Transparency.

Their research is concentrated in the main field of computer science, with 136 publications, prominently within the subfield of artificial intelligence (112 publications). Additional subfields include computer vision and pattern recognition, signal processing, computer networks and communications, and radiology, nuclear medicine, and imaging.

Main topics addressed in their work include:

  • Adversarial Robustness in Machine Learning
  • Privacy-Preserving Technologies in Data
  • Topic Modeling
  • Cryptography and Data Security
  • Natural Language Processing Techniques
  • Advanced Malware Detection Techniques
  • Explainable Artificial Intelligence (XAI)

Frequent collaborators in Florian Tramèr's research are:

  • Nicholas Carlini (38 coauthored papers)
  • Matthew Jagielski (18 coauthored papers)
  • Milad Nasr (13 coauthored papers)
  • Javier Rando (12 coauthored papers)
  • Katherine Lee (10 coauthored papers)

Some notable papers include:

  • "Advances and Open Problems in Federated Learning," 2020, published in Foundations and Trends® in Machine Learning
  • "On the Opportunities and Risks of Foundation Models," 2021, published on arXiv (Cornell University)
  • "Membership Inference Attacks From First Principles," 2022, published at the 2022 IEEE Symposium on Security and Privacy (SP)
  • "Extracting Training Data from Large Language Models," 2020, published on arXiv (Cornell University)
  • "Quantifying Memorization Across Neural Language Models," 2022, published on arXiv (Cornell University)

Best Publications

  • Advances and Open Problems in Federated Learning

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

  • Ensemble Adversarial Training: Attacks and Defenses

    Florian Tramèr;Alexey Kurakin;Nicolas Papernot;Ian J. Goodfellow

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Stealing machine learning models via prediction APIs

    Florian Tramèr;Fan Zhang;Ari Juels;Michael K. Reiter

  • Advances and open problems in federated learning

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

  • The Space of Transferable Adversarial Examples

    Florian Tramèr;Nicolas Papernot;Ian J. Goodfellow;Dan Boneh

  • Membership Inference Attacks From First Principles

    Unknown

  • On Adaptive Attacks to Adversarial Example Defenses

    Florian Tramer;Nicholas Carlini;Wieland Brendel;Aleksander Madry

  • Extracting Training Data from Large Language Models

    Nicholas Carlini;Florian Tramèr;Eric Wallace;Matthew Jagielski

  • Physical Adversarial Examples for Object Detectors

    Dawn Song;Kevin Eykholt;Ivan Evtimov;Earlence Fernandes

  • Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware

    Florian Tramer;Dan Boneh

  • Adversarial Training and Robustness for Multiple Perturbations

    Florian Tramer;Dan Boneh

  • What Does it Mean for a Language Model to Preserve Privacy?

    Unknown

  • SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

    Edward Chou;Florian Tramer;Giancarlo Pellegrino

  • Label-Only Membership Inference Attacks

    Christopher A. Choquette-Choo;Florian Tramer;Nicholas Carlini;Nicolas Papernot

  • FairTest: Discovering Unwarranted Associations in Data-Driven Applications

    Florian Tramer;Vaggelis Atlidakis;Roxana Geambasu;Daniel Hsu

  • Physical Adversarial Examples for Object Detectors

    Kevin Eykholt;Ivan Evtimov;Earlence Fernandes;Bo Li

  • Formal Abstractions for Attested Execution Secure Processors

    Rafael Pass;Elaine Shi;Florian Tramèr

  • SentiNet: Detecting Physical Attacks Against Deep Learning Systems

    Edward Chou;Florian Tramèr;Giancarlo Pellegrino;Dan Boneh

  • Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge

    Florian Tramer;Fan Zhang;Huang Lin;Jean-Pierre Hubaux

  • Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

    Florian Tramèr;Zhicong Huang;Jean-Pierre Hubaux;Erman Ayday

  • Enter the Hydra: Towards Principled Bug Bounties and Exploit-Resistant Smart Contracts

    Lorenz Breidenbach;Philip Daian;Florian Tramer;Ari Juels

Frequent Co-Authors

Dan Boneh
Dan Boneh Stanford University
Nicholas Carlini
Nicholas Carlini Google (United States)
Ari Juels
Ari Juels Cornell University
Nicolas Papernot
Nicolas Papernot University of Toronto
Jean-Pierre Hubaux
Jean-Pierre Hubaux École Polytechnique Fédérale de Lausanne
Dawn Song
Dawn Song University of California, Berkeley
Serge Vaudenay
Serge Vaudenay École Polytechnique Fédérale de Lausanne
Sanjam Garg
Sanjam Garg University of California, Berkeley
Tadayoshi Kohno
Tadayoshi Kohno University of Washington
Somesh Jha
Somesh Jha University of Wisconsin–Madison

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