Voltage imaging, biological time-series modeling, and experiment infrastructure connected to CNEL research at UF.
Six phases of preregistered experiments asking whether a kernel-routed persistent memory helps sequence models: no general advantage yet, but the hypothesis has narrowed from dense radial attention to sparse, top-K-routed separable memory.
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.
Reusable experiment scaffolding for time-series ML: standardized configs, runs, metrics, and plots so model comparisons are fair and repeatable.
A faithful Python reproduction of Li et al. (IEEE MLSP 2011): a spike-time RKHS KLMS decoder that maps spike trains to micro-stimulation, benchmarked against binned KLMS and GLM baselines.