Summary
Zongyi Yang is a Deep Learning R&D Engineer with 16 years of experience at the intersection of robotics, AI, and embedded systems, currently applying end-to-end deep learning to autonomous driving at NVIDIA. He combines high-level algorithm design with low-level microcontroller programming, PCB and mechanical design skills, and a track record in medical imaging research for ultrasonic transducers. Trained at University of Toronto (EngSci) and Carnegie Mellon (Robotics), he brings rigorous academic foundations (GPAs above 3.8) to production-focused projects. Notably, he has built tools to capture expert-labeled data from clinicians and developed real-time tissue/border detection algorithms—an uncommon mix of clinical-domain signal processing and modern deep learning. Based in New Jersey, he excels at bridging sensors-to-ML pipelines, from embedded hardware through deployed neural models.
16 years of coding experience
1 year of employment as a software developer
Bachelor’s Degree, Engineering Science, Electrical and Computer Engineering Major, Robotics and Mechatronics Minor, 3.86, Bachelor’s Degree, Engineering Science, Electrical and Computer Engineering Major, Robotics and Mechatronics Minor, 3.86 at University of Toronto
Master's degree, Robotics, 3.95, Master's degree, Robotics, 3.95 at Carnegie Mellon University
English, Chinese