PhD Candidate | ML for Scientific Discovery | Causal Estimation (REDCLIFF-S) & Individualized Representation Learning
I am an NSF GRFP Fellow and PhD Candidate in Electrical & Computer Engineering under Dr. David Carlson at Duke University. My current work is focused on jointly modeling high and low-frequency signals (neuronal spike traces and local field potentials).
Interests: Machine Learning & Artificial Intelligence, Dynamical Systems Modeling, Hypothesis Generation
Zachary Brown and David Carlson. "Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series." International Conference on Machine Learning, July 2025. Full Paper - Available Soon Bibtex - Available Soon
Zachary Brown, Nathaniel Robinson, David Wingate and Nancy Fulda. "Towards Neural Programming Interfaces." Advances in Neural Information Processing Systems, December 2020. Full Paper BibTeX
Nathaniel Robinson, Zachary Brown, Timothy Sitze, and Nancy Fulda. "Text Classifications Learned from Language Model Hidden Layers." Proceedings of the IEEE 19th World Symposium on Applied Machine Intelligence and Informatics, January 2021. Full Paper BibTeX
Nancy Fulda, Tyler Etchart, William Myers, Daniel Ricks, Zachary Brown, Joseph Szendre, Ben Murdoch, Andrew Carr and David Wingate. "BYU-EVE: Mixed Initiative Dialog via Structured Knowledge Graph Traversal and Conversational Scaffolding." Amazon Alexa Prize Proceedings, November 2018. Full Paper BibTeX
Nancy Fulda, Nathan Tibbets, Zachary Brown, and David Wingate. "Harvesting Common-Sense Navigational Knowledge for Robotics from Uncurated Text Corpora." 1st Annual Conference on Robot Learning (CoRL), 2017. Full Paper BibTeX
Coming Soon!
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Email: zachary.christopher.brown@gmail.com