Christopher Amato

Affiliated Faculty,  Electrical and Computer Engineering
Assistant Professor,  Khoury College of Computer Sciences



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Research Focus

Artificial Intelligence, Machine Learning, Robotics

Honors & Awards

  • 2021 NSF CAREER Award
  • Amazon Research Award (2019, 2020)
  • Best Paper Nominations at RSS-15, AAAI-19, AAMAS-21
  • Best Paper Award AAMAS-14

Research Overview

Artificial Intelligence, Machine Learning, Robotics

Reinforcement learning and planning for partially observable and multi-agent systems

Lab for Learning and Planning in Robotics

With the prevalence of AI and robotics, autonomous systems are very common in all aspects of life. Real-world autonomous systems must deal with noisy and limited sensors, termed partial observability, as well as potentially other agents that are also present (e.g., other robots or autonomous cars), termed multi-agent systems. We work on planning and reinforcement learning methods for dealing with these realistic partial observable and/or multi-agent settings. The resulting method will allow agents to reason about, coordinate and learn to act even in settings with limited sensing and communication.

Lab for Learning and Planning in Robotics


Sep 26, 2018

MIE/ECE Faculty Awarded $1.5M AFRL Grant

MIE/ECE Assistant Professor Jose Martinez-Lorenzo, ECE Professor Tommaso Melodia, ECE Professor Kaushik Chowdhury, ECE/MIE Professor Hanumant Singh, and CCIS/ECE Affiliated Faculty Chris Amato were awarded a $1.5M Air Force Research Laboratory (AFRL) grant for “Robust Decentralized Classification and Coordination Algorithms for Swarms of SUAS.”

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