Assist in maintaining the Academy's Broad Agency Announcement and research regulation; mentor faculty and support their grant applications.

Research on intersection of cyber, human systems, and artificial intelligence.

My primary research focuses on developing psychologically-plausible learning algorithms to both predict human performance and supplement deep learning in complex decision-making tasks in a variety of applied domains. Using both empirical investigation and computational modeling, my research describes how features of the environment become associated together to scaffold the generation of expertise, and how traditionally ‘behavioral’ theories can be expressed in cognitive terms to improve our understanding of bias, persuasion, and influence. My research has been applied to topics in cognitive security, cybersecurity, and explainable artificial intelligence.

Consultant for Intelligent Systems Section – Code 5515 Navy Center For Applied Research in Artificial Intelligence Working with Drs. Greg Trafton and Laura Hiatt, I implemented a theory of associative memory to improve robotics-based computer vision, specifically to improve object identification using associative priming based on prior experience. The algorithm was first justified against human behavioral data from serial memory tasks, and then applied to a cognitive robotics architecture (jACT-R). Future work includes improving performance when recovering from uncertainty or errors in object identification, and improving long-term stability of the algorithms.

Working with Dr. Christian Lebiere, I have conducted in research on sensemaking in intelligence analysts, cybersecurity, behavior prediction, and more generally in cognitive models of human decision-making. My duties included quantitative analyses of human data, determining metrics and algorithms to explain/predict human behavior, and implementing these algorithms in cognitive models. In the context of ICArUS (Integrating Cognitive-Neuroscience Architectures for Understanding Sensemaking), I generated a unified cognitive model of complex sensemaking tasks, which were used to predict the performance of intelligent analysts – more specifically to predict patterns of biased behavior. These algorithms have also been used to justify the use of structured-analytic techniques. In the domain of cybersecurity, these algorithms outperform machine learning in malware identification, leading to a patent of which I am a co-inventor. I have also been a consultant to the Robotics Collaborative Technology Alliance, proposing a statistical learning algorithm to improve information integration on a robotics platform. In addition to the above, I have consulted on the design and analysis social decision-making studies.

At Carleton University I was a full-time Instructor, whose duties included Course Directing 7 different courses and teaching a combined 8 sections totaling over 1000 students. In the 2009/10 and 2010/11 academic years, I taught a 2/1/1 and 2/2 schedule, respectively, which was the maximum allowed for Contract Faculty at Carleton. I primarily taught courses for the Psychology and Cognitive Science Departments. My duties included developing all course materials, supervising multiple teaching assistants, grading, and mentoring student theses. In addition, I have sat as a Department representative on the TA and Contract Faculty Association.

I started a company while in school to do some computer sales and consult on Y2K upgrades for the government. I also assisted in developing building automation system and coding the HVAC operation for a hospital boiler plant, healthcare facility, and sports arena.