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.

Lab Affiliations

All Publications

  • How US law will evaluate artificial intelligence for covid-19. BMJ (Clinical research ed.) Krass, M. n., Henderson, P. n., Mello, M. M., Studdert, D. M., Ho, D. E. 2021; 372: n234

    View details for DOI 10.1136/bmj.n234

    View details for PubMedID 33722811

  • Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning JOURNAL OF MACHINE LEARNING RESEARCH Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., Pineau, J. 2020; 21
  • Separating value functions across time-scales Romoff, J., Henderson, P., Touati, A., Brunskill, E., Pineau, J., Ollivier, Y., Chaudhuri, K., Salakhutdinov, R. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2019
  • Ethical Challenges in Data-Driven Dialogue Systems Henderson, P., Sinha, K., Angelard-Gontier, N., Ke, N., Fried, G., Lowe, R., Pineau, J., ACM ASSOC COMPUTING MACHINERY. 2018: 123-129
  • Deep Reinforcement Learning that Matters Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., Meger, D., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 3207-3214
  • An Introduction to Deep Reinforcement Learning FOUNDATIONS AND TRENDS IN MACHINE LEARNING Francois-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., Pineau, J. 2018; 11 (3-4): 219-354

    View details for DOI 10.1561/2200000071

    View details for Web of Science ID 000453891500001

  • Cost Adaptation for Robust Decentralized Swarm Behaviour Henderson, P., Vertescher, M., Meger, D., Coates, M., Kosecka, J., Maciejewski, A. A., Okamura, A., Bicchi, A., Stachniss, C., Song, D. Z., Lee, D. H., Chaumette, F., Ding, H., Li, J. S., Wen, J., Roberts, J., Masamune, K., Chong, N. Y., Amato, N., Tsagwarakis, N., Rocco, P., Asfour, T., Chung, W. K., Yasuyoshi, Y., Sun, Y., Maciekeski, T., Althoefer, K., AndradeCetto, J., Chung, W. K., Demircan, E., Dias, J., Fraisse, P., Gross, R., Harada, H., Hasegawa, Y., Hayashibe, M., Kiguchi, K., Kim, K., Kroeger, T., Li, Y., Ma, S., Mochiyama, H., Monje, C. A., Rekleitis, Roberts, R., Stulp, F., Tsai, C. H., Zollo, L. IEEE. 2018: 4099-4106
  • OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning Henderson, P., Chang, W., Bacon, P., Meger, D., Pineau, J., Precup, D., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 3199-3206
  • Underwater Multi-Robot Convoying using Visual Tracking by Detection Shkurti, F., Chang, W., Henderson, P., Islam, M., Higuera, J., Li, J., Manderson, T., Xu, A., Dudek, G., Sattar, J., Bicchi, A., Okamura, A. IEEE. 2017: 4189-4196