mathias lecuyer

CS PhD student @ Columbia University

Overview

Mathias Lecuyer
I am a CS PhD candidate at Columbia. Among many other things, I like to build stuff, learn new programming languages, travel, and rock climb. I also love to read, on topics ranging from econ to history and ecology. My research interests span broad areas of computer science, including software systems, security and privacy, statistics and machine learning. My ongoing work focuses on enabling the promises of AI driven ecosystems without imposing undue risks to individuals.

Research

Data has become the principal asset of the Internet era, offering unique opportunities to improve personal and business effectiveness. However this data also poses serious risks to users' privacy and safety, and to organizations by increasing their attack surface and exposing their extensive data stores to external and internal attacks. In my research, I build software systems and machine learning methods to address three major vectors of risk emerging in data driven ecosystems: the aggressive collection and wide access policies often applied to user data; the lack of external transparency on how user data is being used and for what purposes; and new classes of attacks unique to machine learning systems, such as adversarial examples. In each project, my goal is to develop rigorous, theory-backed designs that give clear guarantees and semantics for my tools, systems, and algorithms. I believe that such an approach is necessary to reap the benefits of AI systems without imposing undue risks.
I am fortunate to work with three amazing advisors, Roxana Geambasu, Augustin Chaintreau, and Daniel Hsu ; as well as a number of great collaborators including Riley Spahn and Vaggelis Atlidakis from Columbia, and Siddhartha Sen, Amit Sharma, and Aleksandrs Slivkins from MSR.

Projects

Recent talks

Publications