Published in Harvard DASH, 2023
This thesis discuesses AERO, an adaptive multi-variable optimizing control framework for AAVs.
Recommended citation: Sullivan, Zachary. 2023. AERO: A Neo-Darwinian Approach to Time-Energy Optimal Behavioral Robotics. Masters thesis, Harvard University Division of Continuing Education. https://dash.harvard.edu/handle/1/37374997
Published in Medium, 2020
This paper discusses the potential to further enhance the speed and efficiency of clinical COVID-19 tests and quality assurance procedures through the application of Digital Twin modeling.
Published in Carleton University, 2016
This paper examines prior implementations of finite state behaviors in the context of AI-applied digital games and proposes player augmentation improvements through the use of “fast” reinforcement learning coupled with “slow” policy planning.
Recommended citation: Sullivan, Z. (2016). "Behavioral Valence - Modeling Cohesive Actions to Augment Human Interaction." Carleton University. 1(1). https://www.zacharysullivan.net/_files/ugd/35ba71_a1ebfb97db174e1b9d93f1920353452b.pdf