Projects
When Can Neural Networks Think Longer to Solve Harder Problems?
I stress-tested architectures designed to extrapolate on mazes far harder than their training data, uncovered the shortcut heuristic behind their success, and quantified a data-diversity trade-off. Published at AAAI 2026 (oral presentation).
Read the full story →
maze-dataset: Open-Source Maze Generation for ML Research
I co-authored a peer-reviewed Python package for generating, solving, and visualizing maze datasets, used in neural-network reasoning research with 80+ GitHub stars. Published in JOSS.
Read the full story →
Predicting Building Heights from Satellite-Derived Footprints
At Oak Ridge National Laboratory, I trained XGBoost on 898,000 buildings to predict heights from satellite-visible footprints (0.70 m MAE), and showed that adding coarse global height data doesn’t help.
Read the full story →