I’m a joint JD-PhD (Computer Science) student at Stanford University where I’m lucky enough to be advised by Dan Jurafsky. I’m also an OpenPhilanthropy AI Fellow and a Graduate Student Fellow at the Regulation, Evaluation, and Governance Lab. At Stanford Law School, I help run the Domestic Violence Pro Bono Project. I’m also a Technical Advisor at the Institute for Security+Technology.
Previously, I was lucky enough to be advised by David Meger and Joelle Pineau for my M.Sc. at McGill University and the Montréal Institute for Learning Algorithms. I also spent time as a Software Engineer and Applied Scientist at Amazon AWS/Alexa.
My research focuses on creating robust decision-making systems. My goals are three-fold: (1) use AI to make governments more efficient and fair; (2) ensure that AI isn’t deployed in ways that can harm people; (3) create new ML methods for applications that are beneficial to society.
This involves an eclectic mix of research and fields including: applied and theoretical work in machine learning; investigating reproducible, ethical, sustainable, and thorough research practices and methodologies to ensure that such systems perform as expected when deployed; policy and legal work on the use of AI in government.
- How US law will evaluate artificial intelligence for covid-19. BMJ (Clinical research ed.) 2021; 372: n234
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
JOURNAL OF MACHINE LEARNING RESEARCH
View details for Web of Science ID 000608918500001
Separating value functions across time-scales
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2019
View details for Web of Science ID 000684034305063
- Ethical Challenges in Data-Driven Dialogue Systems ASSOC COMPUTING MACHINERY. 2018: 123-129
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 3199-3206
View details for Web of Science ID 000485488903035
Cost Adaptation for Robust Decentralized Swarm Behaviour
IEEE. 2018: 4099-4106
View details for Web of Science ID 000458872703113
- An Introduction to Deep Reinforcement Learning FOUNDATIONS AND TRENDS IN MACHINE LEARNING 2018; 11 (3-4): 219-354
Deep Reinforcement Learning that Matters
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 3207-3214
View details for Web of Science ID 000485488903036
Underwater Multi-Robot Convoying using Visual Tracking by Detection
IEEE. 2017: 4189-4196
View details for Web of Science ID 000426978204016