I am a Senior C++ System Engineer working in the self-driving industry, with over eight years of professional software engineering experience. I hold a PhD in computer vision acceleration on embedded platforms, where my research focused on prototyping computer vision systems using OpenCV and modern C++, and then optimizing their execution on embedded platforms involving ARM-based C programming and FPGA acceleration. This background gave me a strong foundation in performance optimization,...
I am a Senior C++ System Engineer working in the self-driving industry, with over eight years of professional software engineering experience. I hold a PhD in computer vision acceleration on embedded platforms, where my research focused on prototyping computer vision systems using OpenCV and modern C++, and then optimizing their execution on embedded platforms involving ARM-based C programming and FPGA acceleration. This background gave me a strong foundation in performance optimization, embedded systems, and real-world C++ development.
In my current role, I develop onboard infrastructure (middleware) for autonomous vehicles. My work requires deep expertise in advanced C++ topics such as metaprogramming, multithreading, shared-memory communication, and building reliable, low-latency systems. I also have a strong understanding of the AUTOSAR C++ standard for self-driving vehicles, including best practices and coding styles for safety-critical software.
Alongside my industry work, I have extensive teaching and mentoring experience. I spent four to five years as a graduate-level teaching assistant for courses such as computer architecture and embedded computing, working with students in classroom, small-group, and one-on-one settings. In addition, I have served as a C++ system design interviewer for over four years, which allows me to guide students and working engineers in writing high-quality C++ code, designing robust systems, and preparing effectively for technical interviews. My teaching approach respects different learning styles—whether students learn best through verbal explanation, visual examples, logical reasoning, or hands-on practice—and I tailor each session to match their preferred way of learning and individual goals.