I am a postdoc researcher at Microsoft Research in NY. After this postdoc, I will join the University of British Columbia in Vancouver as an assistant professor (summer 2021). I completed my PhD at Columbia University under the supervision of Roxana Geambasu, Augustin Chaintreau, and Daniel Hsu.
I am broadly interested in machine learning systems, with a specific focus on applications that provide rigorous guarantees of robustness, privacy, and security. My research focuses both on improving practical and theoretical tools (differential privacy, causal inference, reinforcement learning) and enabling specific use-cases (ML attacks/defenses, privacy preserving data management, system decisions optimization).
I am looking for motivated students (and possibly postdocs) to start in Fall 2021.
Publications
- Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu. "Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform" (SOSP'19) [PDF]
- Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana. "Certified Robustness to Adversarial Examples with Differential Privacy" (S&P'19) [PDF][Code]
- Mathias Lécuyer, Riley B. Spahn, Roxana Geambasu, Tzu-Kuo Huang, and Siddhartha Sen. "Enhancing Selectivity in Big Data" (Invited paper, S&P Magazine, 2018) [PDF]
- Mathias Lécuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, and Aleksandrs Slivkins. "Harvesting Randomness to Optimize Distributed Systems" (HotNets'17) [PDF]
- Mathias Lécuyer, Riley B. Spahn, Roxana Geambasu, Tzu-Kuo Huang, and Siddhartha Sen. "Pyramid: Enhancing selectivity in big data protection with count featurization" (S&P'17) [PDF][Long Version][Website]
- Mathias Lécuyer, Max Tucker, Augustin Chaintreau. "Improving the transparency of the sharing economy" (WWW'17) [PDF][Data][Blog post]
- Mathias Lécuyer, Riley Spahn, Giannis Spiliopoulos, Augustin Chaintreau, Roxana Geambasu, and Daniel Hsu. "Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence" (CCS'15) [PDF][Website][The Economist, Slate]
- Nicolas Viennot , Mathias Lécuyer, Jonathan Bell, Roxana Geambasu, and Jason Nieh. "Synapse: New Data Integration Abstractions for Agile Web Application Development" (EuroSys'15) [PDF][Website]
- Mathias Lécuyer, Guillaume Ducoffe, Francis Lan, Andrei Papancea, Theofilos Petsios, Riley Spahn, Augustin Chaintreau, and Roxana Geambasu. "XRay: Increasing the Web's Transparency with Differential Correlation" (USENIX Security'14) [PDF][Website][NYT Bits, MIT Technology Review]
Invited Talks
- Towards a Practical Differentially Private Machine Learning Platform - Northwest Data Science Seminar Series (July 2020).
- Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform - Berkeley Security Seminar (October 2019).
- Security, Privacy, and Transparency Guarantees for Machine Learning Systems - UBC (March 2019).
- Security, Privacy, and Transparency Guarantees for Machine Learning Systems - UCLA (March 2019).
- Security, Privacy, and Transparency Guarantees for Machine Learning Systems - MSR NY (February 2019).
- Certified Robustness to Adversarial Examples with Differential Privacy - UW Security Seminar (October 2018).
- Certified Robustness to Adversarial Examples with Differential Privacy - Berkeley Security Seminar (October 2018).
- Certified Robustness to Adversarial Examples with Differential Privacy - Stanford Security Lunch (June 2018).
- Harvesting Randomness for Counterfactual Evaluation of Systems - Stanford NetSeminar (June 2018).
- Certified Robustness to Adversarial Examples with Differential Privacy - Google Brain (June 2018).
Service
- Program Committees: USENIX Security (2021,2020), Eurosys (2020), SoCC (2019), Systems for ML workshop (2018,2019).
- Reviewer: Journal of Machine Learning Research (2019), ACM Transactions on Internet Technology (2016).