I build signal-processing and machine-learning systems for messy biological data: EEG state-space models, voltage-imaging event detection, and the research software that keeps experiments honest. I am a Ph.D. student in Electrical and Computer Engineering at the University of Florida.
The highest-signal project pages that explain what I am building and why it matters.
Pipeline that turns brain-wave recordings (EEG) into a small set of hidden time-series ‘states’ using a state-space model (Hierarchical Linear Dynamical System, HLDS), then evaluates those states for prediction and separability.
Pipeline for 2D voltage imaging videos that flags ‘events’ (fast, localized changes) using statistical detectors inspired by radar signal processing, producing maps and summaries for downstream analysis.
Reusable experiment scaffolding for time-series ML: standardized configs, runs, metrics, and plots so model comparisons are fair and repeatable.
System that extracts structured nodes (claims, evidence, limitations, etc.) from papers and scores them to support literature review and comparison across a collection.
Standalone projects with their own public homes, separate from the research archive.
A training console for athletes and coaches: programs, diet tracking, progress reports, and coach review in one place.
A practice platform for STEM students built to find the exact idea you missed: attempt a problem, get targeted feedback, repair the gap, and prove it in a new context.
EEG work gets more informative when temporal structure is part of the model instead of something the pipeline averages away.
Papers, citations, code, and the official University of Florida references for this work.