About Me

Research Goals: My research focuses on creating robust decision-making systems grounded in causal inference mechanisms – particularly in natural language domains. I also spend a lot of time investigating reproducible, ethical, sustainable, and thorough research practices and methodologies to ensure that such systems perform as expected when deployed. My other work reaches into policy and technical issues related to the use of machine learning in governance and law, as well as applications of machine learning for positive social impact.

Research Areas: The areas I have dabbled in (or am interested in working on) are: machine learning; reinforcement learning; ethics, fairness, and reproducibility in AI research; metaresearch; natural language processing; empirical legal studies; econometrics; public policy; dialogue systems; robotics.

What did you do before? Previously, I was @ McGill University, Montréal Institute for Learning Algorithms, Amazon Alexa, and Amazon AWS.

Latest News

• Our post on the Health Affairs Blog is up on how we identified high risk buildings for Covid-19 prioritization in Detroit using administrative data. If our methods might be helpful to you, reach out!
• A report (which, again, I was excited to be a part of) has been posted on Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. Check it out!
• A report (which I was excited to be a part of), has been submitted to the Administrative Conference of the United States on the use of AI in Federal Agencies. Check it out!
• Our new paper, Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning” is online! It includes a tool to track and report the carbon emissions and energy consumption of your model.
• We’ve put up a new paper on learning value functions which span different time-scales. It includes a way to learn differences in value functions through a Bellman update!