About Me

I am Dr. Nicholas Waytowich, a Machine Learning Research Scientist with the US Army Research Laboratory, where my primary role revolves around human-guided Machine Learning. Currently, I take pride in being the lead scientist for the ARL’s Human-Guided Machine Learning Branch. Along with my dedicated team of researchers, we are at the forefront of devising innovative algorithms for human-guided AI/ML. My research shines a spotlight on harnessing human feedback through human-in-the-loop machine learning and reinforcement learning. My diverse interests span across deep reinforcement learning, sequential decision-making, natural language processing, human-agent teaming, and human-robot collaboration.

Before my stint with the Army Research Laboratory, I had the privilege of being a postdoctoral fellow under the esteemed Paul Sajda at Columbia University in the Laboratory for Intelligent Imaging and Neural Computing. During this period, I delved deep into creating novel brain-computer interfaces. This pursuit was a continuation of my Ph.D. journey in Biomedical Engineering from Old Dominion University, where I worked closely with Dean Krusienski on practical brain-computer interface applications.

In addition to my research roles, I’ve been sharing my knowledge as an Adjunct Professor at Anne Arundel Community College since 2023. My entrepreneurial venture as the CEO and Co-Founder of AIMS-Technologies, LLC between 2019 and 2020 allowed me to pioneer research strategies to detect and counter non-cooperative, small unmanned aerial systems.

Having had the honor to present my findings at prominent conferences like AAAI, ICML, NeurIPS and AAMAS, I remain committed to pushing the boundaries of what’s possible in my field.

My CV is located here.
My Google Scholar page is here

Selected Publications

PhD Dissertation:

Development of a Practical Visual Evoked Potential Based Brain-Computer Interface
Nicholas Waytowich
ODU Digital Comms, 2015
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Peer-Reviewed Publications:

Negative Obstacle Traversal of Physical Ground Robots via Imitation Learning Based Control
Brian Cesar-Tondreau, Garrett Warnell, Kevin Kochersberger and Nicholas Waytowich
Robotics and Autonomous Systems, 2023

Learning to guide multiple heterogeneous actors from a single human demonstration via automatic curriculum learning in StarCraft II
Nicholas Waytowich, James Hare, Vinicius Goecks, Mark Mittrick, John Richardson, Anjon Basak, and Derrik Asher
SPIE: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 2022
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A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification
Mohit Khatwani, Hasib-Al Rashid, Hirenkumar Paneliyua, Mark Horton, Houman Homayhoun, Nicholas Waytowich, David Hairson, and Tinoosh Mohsenin
Handbook of Biochips, 2022
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Mobile manipulation leveraging multiple views
David Watkins-Valls, Peter Allen, Henrique Maia, Madhavan Seshadri, Jonathan Sanabria and Nicholas Waytowich
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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On games and simulators as a platform for development of artificial intelligence for command and control
Vinicius G Goecks, Nicholas Waytowich, Derrik Asher Song Park, Mark Mittrick, John Richardson, Manuel Vindiola, Anne Logie, Mark Dennison, Theron Trout and others
The Journal of Defense Modeling and Simulation, 2022
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E2hrl: An energy-efficient hardware accelerator for hierarchical deep reinforcement learning
Aidin Shiri, Uttej Kallakuri, Hasib-Al Rashid, Bharat Prakash, Nicholas Waytowich, Tim Oates and Tinoosh Mohsenin
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2022
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Retrospective on the 2021 BASALT Competition on Learning from Human Feedback
Rohin Shah, Steven Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius Goecks, Nicholas Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills and others
Proceedings of Machine Learning Research (PLMR), NeurIPS 2021 Competitions and Demonstrations Track, 2022
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Utility of doctrine with multi-agent RL for military engagements
Anjon Basak, Erin Zaroukian, Kevin Corder, Rolando Fernandz, Christopher Hsu, Piyush Sharma, Nicholas Waytowich and Derrik Asher
SPIE: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 2022
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Towards fully autonomous negative obstacle traversal via imitation learning based control
Brian Cesar-Tondreau, Garrett Warnell, Kevin Kochersberger, and Nicholas Waytowich
MDPI: Robotics, 2022
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TagTeam: Towards wearable-assisted, implicit guidance for human-drone teams
Kasthuri Jayarajah, Aryya Gangopadhyay, Nicholas Waytowich
Proceedings of the 1st ACM Workshop on Smart Wearable Systems and Applications, 2022
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An optimization framework for efficient vision-based autonomous drone navigation
Mozhgan Navardi, Aidin Shiri, Edward Humes, Nicholas Waytowich and Tinoosh Mohsenin
IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022
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Efficient Language-Guided Reinforcement Learning for Resource-Constrained Autonomous Systems
Aidin Shiri, Mozhgan Navardi, Tejaswini Manjunath, Nicholas Waytowich and Tinoosh Mohsenin
IEEE Micro, 2022
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Towards an interpretable hierarchical agent framework using semantic goals
Bharat Prakash, Nicholas Waytowich, Tim Oates, and Tinoosh Mohsenin
NeurIPS Workshop on Language and Reinforcement Learning (LaReL), 2022
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Combining learning from human feedback and knowledge engineering to solve hierarchical tasks in minecraft
Vinicius Goecks, Nicholas Waytowich, David Watkins and Bharat Prakash
AAAI MAKE, 2021
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Human-autonomy teaming for the tactical edge: the importance of humans in artificial intelligence research and development
Kristin Schaefer, Brandon Perelman, Joe Rexwinkle, Jonroy Canady, Catherine Neubauer, Nicholas Waytowich, and others
Systems Engineering and Artificial Intelligence, 2021
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An energy-efficient hardware accelerator for hierarchical deep reinforcement learning
Aidin Shiri, Bharat Prakash, Arnab Mazumder, Nicholas Waytowich, Tim Oates and Tinoosh Mohsenin
IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021
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A Hardware Accelerator for Language-Guided Reinforcement Learning Aidin Shiri, Arnab Mazumder, Bharat Prakash, Houman Homayoun, Nicholas Waytowich and Tinoosh Mohsenin
IEEE Design & Test, 2021
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A flexible multichannel eeg artifact identification processor using depthwise-separable convolutional neural networks Mohit Khatwani, Hasib-Al Rashid, Hirenkumar Paneliya, Mark Horton, Nicholas Waytowich, David Hairston and Tinoosh Mohsenin
ACM Journal on Emerging Technologies in Computing Systems (JETC), 2021
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An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning Nitheesh Manjuath, Aidin Shiri, Morteza Hosseini, Bharat Prakash, Nicholas Waytowich and Tinoosh Mohsenin
IEEE Open Journal of Circuits and Systems, 2021