I am a PhD candidate at Princeton University, where I am also completing a graduate certificate at the Center for Statistics and Machine Learning. I study the impact of machine learning on social decision-making, with a focus on markets and regulation. My work at the interface of AI, finance, law, and sociology asks how data-driven technologies are reconfiguring flows and distributions of information and wealth, in financial markets and beyond. I also investigate complex reasoning in large language models and evaluate whether and how they can be successful at legal reasoning.
I am a co-PI at Princeton’s Debt Collection Lab, where our team studies debt collection lawsuits and inequality in the United States and help make data about private debt collectors accessible to the greater public.
In the private sector, I practiced as a commercial trial lawyer in downtown Montreal (Canada) for five years. I handled high-stakes litigation involving shareholder rights, complex tax planning, and trademark infringement, I advised business owners on their litigation risks, and I participated in a variety of corporate transactions.
Before coming to Princeton, I was at the Faculty of Law of the University of Montreal, where I studied judicial discretion and decision-making by judges and courts, and its relation to socioeconomic history. My thesis has won the 2022 excellence prize of the Quebec Association of Law Professors.
Ph.D. in progress, 2020-...
Princeton University, Department of Sociology and Center for Statistics and Machine Learning
LL.M. (comparative law, with thesis), 2021
University of Montreal
LL.B. (civil law), 2013
University of Sherbrooke
My work at the interface of finance, law, sociology, and computer science and engineering explores how machine learning and computation technologies transform discretionary decision-making under uncertainty, reshape collective intelligence, and in so doing, change the dynamics of the circulation and accumulation of wealth and information in society. I also study what this means for our laws, and what kinds of regulatory frameworks we need to efficiently regulate AI and the data industry.
My dissertation investigates recent changes in the financial data and analytics industry and human-AI interaction in financial markets, and their impact on investment decisions and asset management practices, with an eye to regulatory concerns emerging from algorithmic trading, the changing face of stock exchange groups, and the rise of alternative data. Throughout my computational work, I employ local language models for help with data cleaning, record matching and text analysis.
In a separate line of work, I study complex reasoning in large language models, whether and how they can learn abstract rules in a way that allows them to generalize algorithmically and compositionally, and what this means for applications of AI in the legal field.
Other work in progress: