About me

Welcome! I’m Megan Chiovaro, a Senior Machine Learning Engineer at SEACORP. Our team is working to bring innovative AI/ML solutions to DoD customers. I’ve co-founded and currently co-direct our AI/ML Center of Excellence, creating collaboration and educational opportunities across SEACORP.

My passion for education and computational innovation has led me to join the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island as a part-time Teaching Professor. I developed and have been teaching a new undergraduate course sequence, Machine Learning for Engineering Applications, and the positive feedback and demand has culminated in the founding of the new Undergraduate Certificate in AI for Engineering.

Previously, I’ve also served as a Data Scientist Fellow for the U.S. Census Bureau, using ML to understand links between criminal justice records and IRS returns to identify local labor market opportunities for individuals with criminal records. I’m also an instructor for SoftwareCarpentry, a volunteer-run organization teaching computing skills to researchers and students across the globe.

I earned my PhD in Experimental Psychology from the University of Connecticut, where I was a part of the Dynamics of Social Coordination and Inter-Organism Dependencies (dyscord) Lab, investigating social phenomena in dyads and groups from a dynamical systems perspective. I’m a former Fellow of the Science of Learning and Art of Communication (SLAC) and awardee of an Honorable Mention for the NSF Graduate Research Fellowship Program. I have held affiliations with several other organizations, including the Center for the Ecological Study of Perception and Action, the Institute for Collaboration on Health, Intervention, and Policy, and the Institute for the Brain and Cognitive Sciences.

My academic research interests include social dynamics, collective intelligence, and action coordination in goal-oriented settings. I harness dynamical nonlinear methods and machine learning to capture the wildness and variability that is deep-rooted in sociality. I integrate traditional laboratory experiments and naturally occurring datasets to investigate social coordination from new perspectives. Through these, my work disentangles the particular contexts and constraints which give rise to emergent coordination strategies, in turn leading to increased efficiency and better outcomes in groups.