Christopher Amato

Artificial intelligence
Machine learning
Robotics

PhD in Computer Science, University of Massachusetts, Amherst
MS in Computer Science, University of Massachusetts, Amherst
BA in Clinical Psychology and Philosophy, Tufts University

Christopher Amato is an associate professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.

Amato's research lies at the intersection of artificial intelligence, machine learning, and robotics. He heads the Lab for Learning and Planning in Robotics, where he and his team work on planning and reinforcement learning in partially observable and multi-agent/multi-robot systems. Amato has published widely in leading AI, machine learning, and robotics conferences, receiving a best paper prize at AAMAS-14 and best paper nominations at RSS-15, AAAI-19, and AAMAS-21. He has also co-organized several tutorials on multi-agent planning and learning, and has co-authored a book on the subject.

Before joining Northeastern, Amato was a research scientist at Aptima Inc., a research scientist and postdoctoral fellow at MIT, and an assistant professor at the University of New Hampshire.

Institute for Experiential Robotics

Published: July 5th, 2018
Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning

Citation: Christopher Amato. In the Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), July 2018
Published: February 20th, 2023
Improving Deep Policy Gradients with Value Function Search

Citation: Enrico Marchesini, Christopher Amato. (2023). Improving Deep Policy Gradients with Value Function Search ICLR. https://openreview.net/pdf?id=6qZC7pfenQm
Published: December 13th, 2023
On-Robot Bayesian Reinforcement Learning for POMDPs

Citation: Hai Nguyen, Sammie Katt, Yuchen Xiao, Christopher Amato. (2023). On-Robot Bayesian Reinforcement Learning for POMDPs IROS, 9480-9487. https://doi.org/10.1109/IROS55552.2023.10342114