David J. Cox, PhD, MSB, BCBA-D is a trained bioethicist, behavior analyst, and data scientist. He specializes in the experimental and quantitative analysis of human choice, judgment, and decision-making, and how that understanding can be used to improve the health and well-being of humans and non-human animals. He is especially passionate about leveraging big data and artificial intelligence to improve our fundamental understanding of complex and dynamic behavior-environment relationships.
Insight! Data Science
Post-Doctorate
Data Science
2020
Johns Hopkins University School of Medicine
Post-Doctorate
Behavioral Pharmacology
2020
University of Florida
Ph.D.
Psychology (Behavior Analysis)
2018
Union Graduate College
M.S.
Bioethics
2010
My research focuses on the complex, dynamic interplay between biological organisms` surrounding environment and the behavior they display. In particular, I am interested in practical methods for collecting large datasets that capture as close to the totality of the environment and behavior as possible and leveraging cutting edge analytics (e.g., machine learning) to explore how fundamental principles and behavioral processes interact to determine the choices organisms make. In kind, we can use that information to help people make better decisions in their own daily lives.
ABA 701: History of Behavior Analysis
ABA 710: Conditioning and Learning
ABA 761: Introduction to Scientific Programming in Python
ABA 761: Behavioral Pharmacology
ABA 761: Mathematical & Computational Modeling in Behavior Science
B.F. Skinner New Research Award for the Experimental Analysis of Behavior.
Society for the Advancement of Behavior Analysis Science Translation Award.
Cox, D. J. (2025). Verbal frontiers: Combining words in the wild, computational modeling, and behavior analysis to explore verbal communities. Perspectives on Behavior Science.
https://doi.org/10.1007/s40614-025-00484-y
Cox, D. J., & Santos, C. (2025). Predicting the next response: Demonstrating the utility of integrating artificial intelligence-based reinforcement learning with behavior science. Perspectives on Behavior Science, 48, 241-267.
https://doi.org/10.1007/s40614-025-00444-6
Cox, D. J., & Sosine, J. (2025). A data-driven, algorithmic approach to recommending hours of ABA for individuals with ASD. Behavioral Interventions, 40(2), e70014.
https://doi.org/10.1002/bin.70014