
Peter Henderson
Ph.D. Student in Computer Science, admitted Autumn 2018
Juris Doctor Student, Law
Bio
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.
All Publications
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How US law will evaluate artificial intelligence for covid-19.
BMJ (Clinical research ed.)
2021; 372: n234
View details for DOI 10.1136/bmj.n234
View details for PubMedID 33722811
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Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
JOURNAL OF MACHINE LEARNING RESEARCH
2020; 21
View details for Web of Science ID 000608918500001
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Separating value functions across time-scales
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2019
View details for Web of Science ID 000684034305063
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Ethical Challenges in Data-Driven Dialogue Systems
ASSOC COMPUTING MACHINERY. 2018: 123-129
View details for DOI 10.1145/3278721.3278777
View details for Web of Science ID 000510018100022
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Deep Reinforcement Learning that Matters
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 3207-3214
View details for Web of Science ID 000485488903036
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An Introduction to Deep Reinforcement Learning
FOUNDATIONS AND TRENDS IN MACHINE LEARNING
2018; 11 (3-4): 219-354
View details for DOI 10.1561/2200000071
View details for Web of Science ID 000453891500001
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Cost Adaptation for Robust Decentralized Swarm Behaviour
IEEE. 2018: 4099-4106
View details for Web of Science ID 000458872703113
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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
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Underwater Multi-Robot Convoying using Visual Tracking by Detection
IEEE. 2017: 4189-4196
View details for Web of Science ID 000426978204016