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Contact

Office Location

805 Columbus Avenue
522 Interdisciplinary Science and Engineering Complex (ISEC)
Boston, MA 02120

Mailing Address

Northeastern University
ATTN: Christopher Amato, 435 ISEC
360 Huntington Avenue
Boston, MA 02115

Research Interests

  • Artificial intelligence
  • Machine learning
  • Robotics

Education

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

Biography

Christopher Amato is an assistant professor in the Khoury College of Computer Sciences at  Northeastern University. He received his bachelor’s from Tufts University and his master’s and PhD from the University of Massachusetts, Amherst. Before joining Northeastern, he worked as a research scientist at Aptima, Inc where he managed a project using machine learning to detect patterns of behavior. Amato previously served as a research scientist and postdoctoral fellow at MIT’s Computer Science and Artificial Intelligence Laboratory and the Laboratory for Information & Decision Systems.

His research explores principled solutions for systems of agents with uncertainty and limited communication. Amato’s publication on solving multi-agent problems represented as decentralized partially observable Markov decision processes won a best paper price at AAMAS-14. In addition, his 2015 publication on policy search for multi-robot coordination under uncertainty was nominated for best paper at RSS-15. His work on coordinating teams of robots under uncertain outcomes has been covered by betaBoston (The Boston Globe), Popular Science, the Huffington Post, as well as several other news outlets.

Amato has successfully co-organized several tutorials on team decision making in multi-agent systems. Furthermore, he has co-authored A Concise Introduction to Decentralized Partially Observable Markov Decision Processes (POMDPs) and contributed to Decision Making Under Uncertainty, an academic resource published in 2015. Through his continued dedication to the field, his research seeks to develop fundamental theory and scalable algorithms that provide high-quality autonomy in real-world systems, such as; multi-robot navigation, search and rescue, and surveillance problems. Amato currently heads the Lab for Learning and Planning in Robotics, where his team works on planning and reinforcement learning methods for dealing with multi-agent settings.