research

hotspot (hybrid oceanic tracking via satellite proxy and optimized time-series)

studying how remote sensing and time-series modeling can be used to infer nutrient-driven ecological changes in marine environments.

designed ecological constraint filters and local time-series interpolation to reconstruct missing data and enforce physically consistent predictions.

focused on reducing false positives and improving reliability so environmental forecasts remain usable in real-world monitoring settings.

earned the stockholm regional water prize, placed 3rd at regionals, and advanced to the state science fair.

ai interpretability research: mechanistic interpretability

studying how neural networks represent concepts internally using modern interpretability methods.

analyzed feature discovery, probing, activation patching, and weight-based analysis to understand how models encode and manipulate information.

focused on representation geometry, sparse feature directions, and toy models that reveal mechanisms like superposition and grokking.

synthesized 30+ papers on modern mechanistic interpretability as part of the NCSSM research in computational science program.