I am an assistant professor at the University of British Columbia in Vancouver, where I am part of the Systopia lab, and a member of CAIDA. Prior to this, I was a postdoc researcher at Microsoft Research in NY. Even before, 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).
Contact policy
Prospective students: I only accept applications through the departmental process, with a deadline in December. There is no need to email me about admissions, as I am not able to look at applications or discuss admissions outside of this process. Due to the volume of emails I am unlikely to reply.
Outside of this, please feel free to contact me if you have specific research connections / questions.
Students
Graduate students:- Qiaoyue Tang (PhD)
- Saiyue Lyu (PhD, with Mijung Park)
- Frederick Shpilevskiy (PhD track)
- Jonas Tai (PhD track, with Thomas Pasquier)
- Mishaal Kazmi (MSc, with Ivan Beschastnikh)
- Haley Li (MSc, with Thomas Pasquier)
- Amir Sabzi (MSc, with Aastha Mehta)
- Shadab Shaikh (MSc)
- Bingshan Hu (with Nidhi Hegde and Mark Schmidt)
- Helen Chen
- Joel Hempel
- Ryan Shar
- Mauricio Soroco
- Eric Xiong
- Frederick Shpilevskiy (Honors thesis, with Margo Seltzer) → PhD student at UBC
- Shiqi He (MSc, with Ivan Beschastnikh) → PhD student at the University of Michigan
Publications & Preprints
- Qiaoyue Tang, Mathias Lécuyer. "DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation" (RTML workshop at ICLR 2023) [arXiv]
- Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang, Mathias Lécuyer, Ivan Beschastnikh. "GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning" (MLSys 2023) [arXiv][code]
- Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury, Asaf Cidon, Roxana Geambasu, Mathias Lécuyer, Junfeng Yang. "Packing Privacy Budget Efficiently" (preprint 2022) [arXiv]
- Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo Seltzer. "Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design" (AIMLAI Workshop 2022) [arXiv full version]
- Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen. "Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments" (ICML 2022) [arXiv][code][demo]
- Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins. "Sayer: Using Implicit Feedback to Optimize System Policies" (SoCC 2021) [PDF]
- Tao Luo, Mingen Pan, Pierre Tholoniat, Asaf Cidon, Roxana Geambasu, Mathias Lécuyer. "Privacy Budget Scheduling" (OSDI 2021) [PDF][Long Version][Code]
- Mathias Lécuyer. "Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning" (Preprint 2021) [arXiv]
- 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 Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu. "Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform" (SOSP'19) [PDF]
- Mathias Lécuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu. "Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform" (OSR'19) [PDF]
- 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]
Teaching
I teach/taught the following classes:- CPSC 532Y / 538L: Causal Machine Learning (Fall 2023, Fall 2022)
- CPSC 538L: Differential Privacy - Theory and Practice (Spring 2022)
Invited Talks
- [Tutorial] Privacy as hypothesis testing: linking Differential Privacy, membership attacks, and privacy audits - Canadian AI, Responsible AI Track, Montréal (June 2023).
- DP-AdamBC: your DP-Adam is actually DP-SGD (unless you apply Bias Correction) - Bridge the gap: Differential Privacy and Statistical Analysis, Amii Upper Bound Workshop, Edmonton (May 2023).
- 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 Committee: SatML (2024), OSDI (2023), Security & Privacy (Oakland) (2023, 2022), USENIX Security (2021, 2020), MLSys (2021), Eurosys (2020), SoCC (2019), Systems for ML workshop (2019, 2018).
- Reviewer: ICML(2022, 2020), NeurIPS (2021), Journal of Machine Learning Research (2019), ACM Transactions on Internet Technology (2016).