Publications
Papers, posters, and the reproducible artifacts behind each one.
Submitted / under review
E. Hackman, M. Sakyi, H. Zhu. Measurement-aware physics-informed neural networks for estimating dopamine diffusion and uptake from sparse electrode recordings. Submitted to Biomedical Signal Processing and Control (Elsevier), October 2026. [Code & data] · [GitHub]
J. Ding, E. Hackman, H. Zhu. Difference equations and limits in geometry. Submitted to The American Mathematical Monthly (Taylor & Francis), September 2026.
Thesis
E. Hackman. Scalable high-resolution simulation of the prey-predator model, with a non-local consumption of prey. MS thesis, University of Southern Mississippi, 2025. The Aquila Digital Community — available via Google Scholar.
Software & artifacts
dopamine-PINN (2026). JAX/Flax code, data and results for the dopamine paper, MIT License. [Zenodo] · [GitHub]
Talks
To be added.
Posters
E. Hackman, H. Zhu. Inverse physics-informed neural networks for synaptic dopamine transport: parameter recovery at the information limit of sparse, noisy data. 11th SIAM Central States Section Annual Meeting, Jackson State University, Jackson, MS, October 10–11, 2026. Third Place Poster Award.
E. Hackman, H. Zhu. When inverse physics-informed neural networks become practically identifiable: a case study in synaptic dopamine transport. Mississippi IDeA/EPSCoR Conference 2026 (MIEC26), Hattiesburg, MS, July 2026.