Biomedical Engineering
The core discipline: how engineering thinking maps onto biology, the body, and clinical reality.
›Signals & systems in physiology
›Design for the human body
research lab
ResearchNotebook
Not a publication list, just a working notebook of what I'm studying, why it matters, and the threads I'm pulling on.
Bioinformatics
Turning genomic and biological data into something interpretable, with an emphasis on traceable, deterministic methods.
›Genomic pipelines
›Explainable health insights
Medical Devices
How devices are designed, validated, and made safe, and where AI can responsibly assist instead of obscure.
›Safety & validation
›Human factors
Sensors & Embedded Systems
Capturing real-world signals cleanly: the analog edge, microcontrollers, and the path from sensor to insight.
›Signal capture & noise
›Edge compute
Human-Computer Interaction
How people actually interact with systems, gesture, voice, ambient interfaces, and accessibility.
›Gesture & voice input
›Ambient / projected UI
AI for Personal & Biomedical Systems
Where local AI agents can genuinely support people and clinical workflows, without pretending to be a doctor.
›Local agents
›Assistive workflows
open threads
Questions I'm Exploring
The questions driving the reading. None of these have clean answers yet, and that's why they're interesting.
?
How can local AI systems support biomedical workflows?
Privacy, latency, and trust all push toward running models locally, so what does that unlock for clinical and lab work?
?
What would a personal biomedical assistant look like?
Not a chatbot, an assistant that understands your sensors, your context, and where its own limits are.
?
How can computer vision make environments more accessible?
If a room can see and understand intent, it can respond to people who can't reach a switch or a screen.
?
How can sensors and embedded systems improve human-centered design?
Better signals mean better feedback loops, and a chance to design around real bodies, not assumptions.