I build machine-learning systems for hard physical and biological data: surrogate models for computational fluid dynamics in the SmartDATA Lab, event detection and source separation for neural imaging, 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.
Ph.D. research in the SmartDATA Lab: learned surrogates that stand in for expensive CFD solvers, judged on stability, physical consistency, and generalization.
A proposal for a transformer alternative: use a learned radial geometry for local context, a finite bank of persistent supports for reusable structure, and a lightweight online readout for adaptation after distribution shift.
Pipeline that maps noisy multichannel time-series observations to compact latent trajectories with a Hierarchical Linear Dynamical System (HLDS), then evaluates those trajectories for classification, separability, and stability.
Research platform for zebrafish neural-imaging video: statistical event detection for voltage recordings and a calcium-imaging experiment program that separates activity from background, artifacts, and noise.
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.
The strongest result was not a single winning denoiser: preserving the activity carrier and using spatial separation features for ranking proved more reliable.
Papers, citations, code, and the official University of Florida references for this work.