View on GitHub

Algorithmic Alignment Group

Researching frameworks for human-aligned AI @ MIT CSAIL.

Team

Principal Investigator

Dylan Hadfield-Menell Dylan Hadfield-Menell, dhm@csail.mit.edu, Website
Dylan is an assistant professor on the faculty of Artificial Intelligence and Decision-Making in the EECS Department and Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). His research focuses on the problem of agent alignment: the challenge of identifying behaviors that are consistent with the goals of another actor or group of actors. His work aims to identify algorithmic solutions to alignment problems that arise from groups of AI systems, principal-agent pairs (i.e., human-robot teams), and societal oversight of ML systems.

Ph.D Students

Andreas Haupt Andreas Haupt, haupt@csail.mit.edu, Website
Andy is an interdisciplinary Ph.D. Candidate with the Schwarzman College of Computing. He uses tools from Microeconomic Theory to understand multi-agent systems, recommendation engines, and automatic pricing tools in deployment and propose ways to mitigate undesirable consequences of seemingly innocuous algorithmic choices.

Jovana Kondic Jovana Kondic, jkondic@mit.edu, LinkedIn
Jovana's interests lie broadly at the intersection of probabilistic inference, social cognition, and human-robot interaction. Her research focuses on building interactive AI agents that 1) effectively learn from human input, and 2) understand and act in accordance with human preferences, intentions, and values.

Phillip Christoffersen Phillip Christoffersen, philljkc@mit.edu, Website
Phillip is broadly interested in reinforcement learning topics including AI alignment, neurosymbolic AI, and multi-agent RL. Before the Algorithmic Alignment Group, Phillip was an undergraduate researcher at the University of Toronto, advised by Prof. Sheila McIlraith. His main hobbies include reading, playing piano, and composing music.

Stephen Casper Stephen Casper, scasper@mit.edu, Website
Cas works on tools for trustworthy, safe AI. His research emphasizes interpretability, adversaries, and robust reinforcement learning. Before his Ph.D, he worked with the Harvard Kreiman Lab and the Center for Human-Compatible AI. He's also a member of the Effective Altruism community. Hobbies of his include biking, growing plants, and keeping insects.

Masters Students

Dana Choi Dana Choi, choie@mit.edu
Dana is broadly interested in understanding how the human normative system works and what enables cooperation. Through reverse-engineering the mechanisms of human collective intelligence, she hopes to contribute to efforts in designing and facilitating desirable interactions in our society. She draws insights from economics, anthropology, cognitive science, reinforcement learning, and social computing.

Olivia Siegel Olivia Siegel, osiegel@mit.edu, LinkedIn
Olivia is a masters student interested in AI and robotics who gets most excited seeing algorithms come to life on physical robots. Prior to joining the Algorithmic Alignment group, she did her undergraduate at MIT in EECS and worked on soft robots in the Distributed Robotics Lab. Like any good New Englander, her hobbies include shellfishing, cycling, and maple syrup making.

Rui-Jie Yew Rui-Jie Yew, rjy@mit.edu, Website
Rui-Jie is an S.M. student in Technology and Policy doing research on regulatory mechanisms for AI. She is broadly interested in the societal impacts of AI and the organizational and policy incentives surrounding its development. Previously, she completed a joint BA in computer science and math from Scripps College as an off-campus major at Harvey Mudd.

Former Students

Magdalena Price Magdalena Price, maprice@mit.edu
Lena is a first-year M-Eng student whose research focuses on human-computer interfaces and machine learning. Her current project is focused on making effective data preparation scalable and inexpensive, in the hopes of mitigating biased model results commonly seen in big data predictions.

Max Langenkamp Max Langenkamp, maxnz@mit.edu
Max is researching AI governance. His current focus is on open source machine learning software and how it shapes AI research. He is especially inspired by the work of economist Elinor Ostrom and draws from fields ranging from the philosophy of science to the economics of innovation. Previously, he has researched computational cognitive science, worked at the White House Office of Science and Technology Policy, and published on AI policy at the Center for Security and Emerging Technology. He has a B.A. from MIT in computer science and loves bossa nova, Tibetan mythology and the history of technology.