About
Hi — I’m JeongHyun [Vaughn] Lee, an applied engineer and researcher working across embedded systems, energy storage, automation, and single-cell bioengineering.
My work connects scientific precision with large-scale engineering reliability — from isolating a single cell inside a hydrogel microwell to ensuring stability, fault tolerance, and data observability in marine-scale battery energy storage systems.
Identity
Engineer · Researcher · System Builder
Making complex systems observable and reliable.
What I Focus On
I like solving problems where:
- systems behave unpredictably
- data is incomplete or hard to interpret
- failure modes emerge only under real-world conditions
- measurement and observability define success
Whether in a biological experiment or a distributed embedded system, I care about the same things:
Measure it. Understand it. Make it reliable.
Core Domains
| Area | Focus |
|---|---|
| Embedded Systems & Firmware | BMS behavior, Modbus/MQTT communication, latency, fault behavior, regression testing |
| Automation & DevOps for Hardware | Python-based test automation, CI pipelines, telemetry-driven validation |
| Data & Machine Learning | Time-series anomaly detection, voltage/current/thermal event analysis, telemetry infrastructure |
| Research & Bioengineering | Microfluidics, hydrogel patterning, microscopy, single-cell RNA-seq, system design |
Work Background
- Marine ESS BMS — Embedded Software QA Engineer
Firmware validation, safety logic verification, telemetry analysis, automated regression pipelines. - Postdoctoral Research — UBC
Image-guided cell selection, microfabrication, sequencing workflows, high-dimensional biological data. - PhD Research — UBC Mechanical Engineering
Developed See-N-Seq: a method enabling RNA sequencing from visually identified single cells using micropatterned hydrogels.
Transition from Research to Engineering
A lot of people ask whether moving from biomedical science to energy systems was a big jump.
The surprising answer is: not really.
Single-cell sequencing and large-scale battery systems share common learning patterns:
- both require rigorous validation
- both can fail silently without correct observability
- both demand accuracy, repeatability, and system-level thinking
- both require understanding what went wrong, not just whether it worked
The domain changed — the thinking didn’t.
What I'm Building Now
- A structured portfolio documenting both research and engineering
- A space to write about debugging, architecture, design choices, and lessons learned
- A reference I can return to (and improve) as my work evolves
This site is still growing — and that’s intentional.
Connect
📩 Email:
🔗 LinkedIn
🐙 GitHub
📚 Publications → /publications
Thanks for reading — and welcome.