Mathias Lécuyer
About
Appointed Canada CIFAR AI Chair – 2026
Mathias works on trustworthy AI, on topics ranging from privacy, robustness, explainability and causality, with a specific focus on applications that provide rigorous guarantees. Recent impactful contributions include: the first scalable defense against adversarial examples (small changes to inputs that can control AI models predictions and be used for AI jailbreaks) with provable guarantees; a technique to efficiently measure the influence of training data on AI model behavior; a method to audit privacy leakage from AI models given only API access; and a system to enable federated and privacy preserving measurements of advertising performance, now serving as the blueprint for a future standard aiming to reduce third party tracking on the web.
Awards
- Distinguished Reviewer Award, 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) (2025)
- ACM Symposium on Operating Systems Principles Distinguished Artifact Honorable Mention (2024)
- Google Research Scholar award (2022)
Relevant Publications
- Kazmi, M., Lautraite, H., Akbari, A., Tang, Q., Soroco, M., Wang, T., ... & Lécuyer, M. (2024). Panoramia: Privacy auditing of machine learning models without retraining. Advances in Neural Information Processing Systems, 37, 57262-57300.
- Lyu, S., Shaikh, S., Shpilevskiy, F., Shelhamer, E., & Lécuyer, M. (2024). Adaptive randomized smoothing: Certified adversarial robustness for multi-step defences. Advances in Neural Information Processing Systems, 37, 134043-134074.
- Tholoniat, P., Kostopoulou, K., McNeely, P., Sodhi, P. S., Varanasi, A., Case, B., ... & Lécuyer, M. (2024, November). Cookie monster: Efficient on-device budgeting for differentially-private ad-measurement systems. In Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles (pp. 693-708).
- Lin, J., Zhang, A., Lécuyer, M., Li, J., Panda, A., & Sen, S. (2022, June). Measuring the effect of training data on deep learning predictions via randomized experiments. In International Conference on Machine Learning (pp. 13468-13504).
- Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana, S. (2019). Certified Robustness to Adversarial Examples with Differential Privacy. In 2019 IEEE Symposium on Security and Privacy (SP) (pp. 656-672).