Start building before you feel ready
My first app was a calculator with no decimal support. It shipped anyway, and it taught me more than any textbook. Momentum beats perfection.
For Students
My Journey
Notes, lessons, and resources for students and aspiring engineers who want to build things that matter.
The Short Version
I built my first app at 13: a calculator in Xcode with no decimal support. I built my first circuit at 15: a piano made of 555 timers. I discovered water quality problems at my school at 16 and turned it into a science fair project that taught me more about engineering than any class.
At Clarkson University, I went from undergraduate to graduate researcher, publishing papers on autonomous wheelchairs and assistive vision systems at IEEE. My master's thesis tackled post-quantum cryptography, securing file transfers against threats that don't exist yet.
Today I verify AI acceleration silicon at Microsoft and build Huitzo, a self-hosted AI runtime for regulated enterprises, independently. I've published six papers across IEEE conferences and arXiv, and learned that the most important skill isn't any single technology. It's the ability to learn fast, build fast, and care about the people your work serves.
Lessons I Wish I Knew Earlier
The best learning happens at intersections
My strongest work came from combining fields: hardware + software, research + entrepreneurship, engineering + design thinking. Don't silo yourself.
Publish your work, even if it's not perfect
Every IEEE paper I published had things I'd change. But putting work out there created opportunities I never expected: collaborations, citations, and credibility.
Build things that help people
The projects I'm most proud of, the wheelchair and the vision glasses, were driven by real human need. Technology matters most when it serves someone.
Your background is your superpower
I came from a non-traditional path. That perspective of seeing problems differently turned out to be the most valuable thing I brought to every team.
Say yes to the hard problems
Post-quantum cryptography, AI silicon, embedded ML: these felt impossible at first. But hard problems are where the growth is, and where fewer people compete.
Resources I Recommend
Learn SystemVerilog
chipverify.com: free, comprehensive, and practical for hardware verification.
Embedded Systems
Start with Arduino, graduate to STM32. Build something physical early.
Cloud & Infrastructure
Docker → Kubernetes → Terraform. Build one real deployment before learning the next tool.
AI/ML Foundations
Andrew Ng's courses remain the best starting point. Then build something with real data.
Research Writing
Read 10 papers in your field before writing one. Understand the structure and conventions.
Startup Thinking
Y Combinator's Startup School is free and honest about what building a company actually takes.