Featured Presenters

Vanessa Moody
Scientist

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Vanessa Moody
Scientist

Vanessa Moody’s work at Charles River focuses on machine learning and deep reinforcement learning (RL), clustering and dimensionality reduction techniques, and biomedical data analysis. Under the DARPA XAI and CAML programs, she contributed significantly to the development of explanatory tools employing our Neural Program Policies (NPPs), and led the development of Counterfactual Explanation Generators.

Moody has also expanded our suite of workflow analysis tools to include a classification model for strategy inference in a multi-agent environment. As technical lead on the JUPITER project, she surveyed AI in mission planning, identified potential contributions for XAI, and prototyped a system for joint human-AI mission planning. As technical lead on DISCERN, she’s building cluster-based explanatory tools for deep RL agents.

Moody is the co-author (with Stephanie Kane and Michael Harradon) of “Towards Incorporating AI into the Mission Planning Process” and (with Jeff Druce, James Niehaus, David Jensen, and Michael L. Littman) of “Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program.” She earned a B.S.E. in Bioengineering from the University of Pennsylvania, and is currently pursuing a master’s degree in data science at the Harvard Extension School.

Michael Harradon
Senior Scientist

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Michael Harradon,
Senior Scientist

Mr. Harradon holds a M.Eng. in Computer Science and a B.S. in Physics and Computer Science, both from MIT, where he also won first place in the 2016 100K Pitch Competition. His primary research interests are machine learning and reinforcement learning (RL), and he is an expert in probabilistic AI, including probabilistic programming. He’s applied novel machine learning approaches to a range of domains, including financial data analysis, radar signals processing, and computer vision. Other areas of expertise include signals analysis, game theory, optimization methods, and genetic algorithms.

Harradon is the inventor of Neural Program Policies (NPPs) and a pioneer in the NPP approach to explainable-by-construction RL agents. As principal investigator for several projects funded under DARPA’s OFFSET program, he is applying NPPs to automatically learn drone swarm behaviors in urban settings. For PERCIVAL-MERLIN, he used NPPs to develop RL-trained agents that predict and track surface vessels.

Harradon has led numerous other projects at Charles River Analytics. As the principal investigator on the STAPLES project (under the DARPA LogX program), he is leading the development of new probabilistic models and inference algorithms for logistics forecasting.

Harradon is a key figure in Charles River’s leadership in the emerging field of explainable AI; he works on explainable modeling and deep RL for the CAMEL effort and he developed novel approaches to identifying human intelligible concepts for the ALPACA project. He is the co-author (with Jeff Druce and Brian Ruttenberg) of “Causal Learning and Explanation of Deep Neural Networks Via Autoencoded Activations” and co-author (with Jeff Druce and James Tittle) of “Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems.”

James Niehaus
Principal Scientist and Director

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James Niehaus
Principal Scientist and Director

Dr. Niehaus specializes in the computational modeling of narrative. As the principal investigator for multiple efforts to develop new training technologies, Niehaus has developed game-based training and virtual coaches for a wide range of audiences; investigated the neural, cognitive, and behavioral relationship between implicit learning and intuition; and worked on social robots for Alzheimer’s patients.

With more than 15 years’ experience applying AI to training systems and instructional design, Niehaus is a highly valued mentor and collaborator for other Charles River scientists and engineers, contributing to a variety of virtual and adaptive systems for medical training. He’s also worked on narrative generation projects, cultural interaction with virtual agents, and cognitive models of discourse comprehension.

Niehaus has a B.S. in computer science from the College of Charleston and a Ph.D. in computer science from North Carolina State University. He is a member of the Society for Simulation in Healthcare and of the National Training and Simulation Association. His extensive publications list includes “Designing Serious Games to Train Medical Team Skills” (with Ashley McDermott and Peter Weyhrauch), “Applications of Virtual Environments in Human Factors Research and Practice” (co-author), and “Development of Virtual Simulations for Medical Team Training: An Evaluation of Key Features” (with Ashley McDermott, Peter Weyhrauch, et al.)