SmartDATA Lab
Physics & simulation projects
Machine-learning surrogates for computational fluid dynamics and physics simulation with the SmartDATA Lab at UF: neural operators, mesh-based networks, and physics-informed training.
Research overview
Research, publications, and public proof
My current research builds machine-learning surrogates for physics simulation in the SmartDATA Lab, alongside neuroengineering work in CNEL and engineering, workshop, and prototype work through IEEE SPS at UF. The common thread is modeling that remains useful when the data is noisy, the physics is unforgiving, and the deployment constraints are real.
CNEL
CNEL projects
Voltage imaging, biological time-series modeling, and experiment infrastructure connected to CNEL research at UF.
IEEE SPS @ UF
IEEE SPS projects
Student-facing research engineering, workshops, neurotechnology prototypes, robotics, and open-source systems through IEEE SPS at UF.
Featured project pages
Canonical project pages with collaborators, scope, and related work.
Learning surrogate models for computational fluid dynamics
Ph.D. research in the SmartDATA Lab: learned surrogates that stand in for expensive CFD solvers, judged on stability, physical consistency, and generalization.
Neural operatorsMesh graph networksPhysics-informed trainingLatent dynamics
Kernel Adaptive Memory: a transformer alternative for sequence modeling
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.
Radial kernel attentionPersistent memory supportsTransformer comparisonOnline NLMS adaptation
Latent-signal classification with hierarchical dynamical systems
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.
Hierarchical Linear Dynamical System (HLDS)Latent-signal classificationTemporal representation learning
Zebrafish neural imaging: event detection & source separation
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.
Voltage & calcium imagingEvent detectionSource separationStatistical detection
Publications, talks, and public proof
Papers, talk recordings, and official University of Florida coverage connected to the projects above.
Preprint
Plato's Cave: A Human-Centered Research Verification System
March 2026 · arXiv
This preprint documents the Plato's Cave system for structured claims, agent-based verification, and human-centered research review.
Research identity
Persistent profiles
ORCID, Google Scholar, and the CV provide the shortest verification path for academic visitors.
Talk
Foundations of Signal Processing
2025 · UF Data Science & Informatics · DSI Spring Symposium
Public signal-processing workshop recording connected to IEEE SPS at UF.
Official coverage
UF coverage and recognition
University pages connect the work to UF AI, UF ECE, and public award context.
Writing connected to the work
Short public notes that explain what I care about, what I am learning, and where the research is moving.
Source separation for zebrafish calcium imaging
The strongest result was not a single winning denoiser: preserving the activity carrier and using spatial separation features for ranking proved more reliable.
calcium imagingsource separationsignal processingneuroengineering
Why I care about state-space models for EEG
EEG work gets more informative when temporal structure is part of the model instead of something the pipeline averages away.
eegstate-space modelssignal processing
Building an EEG/EMG helmet from scratch
Biosignal research gets cleaner when hardware, firmware, and modeling are treated as one system instead of three separate concerns.
eegemghardwareneuroengineering