Biomedical Engineering × AI Systems × Creative Engineering
I build AI systems and engineering projects that make technology feel more personal and interactive, with a focus on cardiovascular and medical technology, from a local “Jarvis” room assistant to biomedical and computer-vision experiments.
A live snapshot of what the operator is running, learning, and chasing.
Local AI core with subagents, connectors, and a projector orb.
AI agents, bioinformatics, computer vision, MCP, local models.
Building from first principles instead of stitching black boxes.
Always a prototype in flight. Nothing here is finished.
“I was born with a heart that needed engineering to keep working. Now I want to build the technology that helps the next kid.”Read why I build →
Jarvis runs language locally through Ollama, dispatches work to focused subagents, controls room lighting, and renders itself as a glowing orb on the wall via a projector. Vision experiments (hand tracking, object ID) run alongside the core.
The through-line under everything: using engineering and AI to work with the human body, not around it.
Turning genomic and wearable signals into traceable, explainable insight, determinism over black boxes.
Capturing real-world physiological signals cleanly, from the analog edge to the screen.
Where local AI agents can genuinely support people and clinical workflows without overreaching.
The honest record of what changed, what broke, what I learned.
What I've been falling into, month by month.
Turning terse sequencing files into something you can actually read.
Wiring local intelligence into the physical room.
A cross-section of what's on the bench, some shipping, most still prototypes.
A local AI room system with a projector orb persona, subagents, connectors, and computer-vision experiments. The flagship.
Orchestrating local models, tools, and hardware is mostly a latency and state-management problem, not a model problem.
A real 3D intra-ventricular blood-flow simulator bolted onto my cardiac digital twin, a GPU fluid engine that streams the mitral vortex and aortic jet into a live desktop view, instead of faking flow with scalar arrows. The most personal engineering on this list: I model the heart I was born with.
Choosing the method was the real decision. OpenFOAM's per-step case-file shuttling on Windows makes interactive stepping impossible, so a Python-native GPU Lattice-Boltzmann solver was the only thing fast enough to step the flow live.
A hackathon CNN that reads cardiac 'enhancer' DNA and predicts which single-letter noncoding variants might disrupt heart-gene regulation, a research-priority ranker for Tetralogy of Fallot and related congenital heart disease.
The subtle bug was saturation: an unregularized model pushes every positive to probability 1.0, which hides the tiny signal from a single-base change. Adding weight decay and dropout made variant effects visible again, and raised AUROC at the same time.
A clinician-facing safety, evidence, and documentation audit layer for personalized health protocols, a preflight checklist that flags risks before a protocol reaches a patient. (Mockup, not medical advice.)
In health software, an auditable explanation beats a confident recommendation. The engine should surface what must be reviewed, not pretend to decide.
A custom CAD-designed, 3D-printed mount to bolt a laptop cleanly onto a sim-racing rig, measured, modeled, printed, iterated.
Real parts punish optimistic tolerances. Designing for the printer's quirks beats designing for the ideal.
An experiment in turning raw genomic + wearable signals into deterministic, explainable health insights, no black boxes.
In health, an explanation you can trace beats a prediction you can't. Determinism is a feature, not a limitation.
Open to internships, research, and collaborations across biomedical engineering and AI systems.
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