My current research focuses on biologically inspired computer vision and perception. My work is in understanding the computational circuits that underlie robustness, adaptability and modeling.
I am a Principal Research Scientist at NAVWAR in San Diego, where I founded and direct the NeuroML and NeuroVision labs. I am completing my Ph.D. in Data Science at UC San Diego as a DoD SMART Scholar, advised by Dr. Virginia de Sa in Cognitive Science and Prof. Gary Cottrell in CSE. Previously I received my M.S. in Computer Science from Georgia Tech, where I was advised by Prof. David Joyner and Prof. Ashok Goel, and my B.S. in Electrical Engineering from the University of Rochester.
Conference on Cognitive Computational Neuroscience (CCN) · 2026
Two widely used memorability predictors track pixel-level colour and frequency statistics rather than the people and scenes humans actually remember — imperceptible perturbations move their scores far more than the presence of a person does.
arXiv:2310.07941 · 2023
Adapting convolutional networks for real-time cortical classification during mobile EEG, moving brain imaging out of the lab and into free movement.
IEEE Signal Processing in Medicine and Biology (SPMB) · 2021 · pp. 1–5
A U-Net autoencoder that removes eye-blink artifacts from EEG, outperforming ICA on low-electrode setups where ICA has too few channels to work with.
Proc. SPIE 11733, Geospatial Informatics XI · 2021 · 1173305
Using synthetic imagery to determine the spatial resolution actually required to detect vessels, rather than procuring for it by guesswork.
Eleven perceptual-similarity metrics evaluated on the full NIGHTS corpus rather than its unanimous-agreement subset. Untrained cosine distance on a frozen self-supervised backbone reaches 74.1%, while a split-half estimate shows the majority labels themselves reproduce only 70.1% of the time — the extra accuracy from supervised training reflects matching the collected votes, not closer agreement with human judgment.