Growing up in Taichung, I was always curious about how things worked—and what lay beyond what I could see. That curiosity deepened when I discovered Silicon Valley (2014–2019). The show captured something I hadn't seen before: people just building things. Lots of things. From messy apartments to half-baked startups. They didn't always succeed, but they kept shipping. That moment shifted something for me. If they could do it, why couldn't I?

But I also noticed something else. Taiwan's hardware semiconductor industry was world-class - TSMC, Delta Electronics, MediaTek, Foxconn, the whole ecosystem. Yet our software story still felt incomplete. I decided I wanted to be part of bridging that gap: connecting Taiwan's hardware strength with software innovation to build products that actually matter.

National Yang Ming Chiao Tung University gave me the foundation. B.Eng. and M.Eng. in Electrical & Computer Engineering. More importantly, those years taught me how to keep learning. I discovered my biggest strength wasn't any particular technology—it was slowing down, looking deeper, and finding patterns in messy problems. That mindset still guides how I work today.

My internships at ITRI and GallopWave were my first chance to connect theory with reality. I built motion-forecasting networks for real local Taiwan's traffic. I designed 3D point-cloud pipelines for L4/L5 autonomous vehicles. I saw how good engineering isn't about perfect theory—it's about making things work reliably when real data gets messy and unpredictable.

At InQuartik, a patent-intelligence SaaS company, I shifted from research robotics to products that real users depended on. This was terrifying at first. Someone's business, someone's payroll, someone's actual problem if my code broke. I learned about maintainable architecture. CI/CD. Collaborating with product teams who needed features yesterday. I also learned how fast market needs change what we build—and how quickly we must adapt.

Now I'm at Upbeat Technology, a venture team building RISC-V SoCs with dedicated AI accelerators. We're designing neural network stacks from research to production silicon. Since I joined, I've watched the team grow, our product move from R&D to production, and revenue multiply. Beyond the technical work, I'm learning something new: what it actually takes to turn an idea into a company.