Summary
Caiwen Ding is an Associate Professor of Computer Science & Engineering at the University of Minnesota who combines a decade of research and engineering experience across machine learning systems, computer architecture, and non-von Neumann/neuromorphic computing. He earned his PhD from Northeastern and led impactful projects on FPGA/GPU acceleration, ReRAM in-memory computing, and privacy-preserving ML, with publications across top conferences and multiple best-paper recognitions. His work translates between theory and hardware: he has a track record of building efficient DNN accelerators and winning the 2022 TinyML contest for accuracy, reflecting practical system-level expertise. Having taught large graduate courses and engineered full-stack backend and IoT solutions, he blends deep academic rigor with hands-on software and hardware development. Based in Shanghai and with experience spanning U.S. universities, he often bridges ML research, secure/specialized hardware, and production-focused system design.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD) Electrical and Computer Engineering, Doctor of Philosophy (PhD) Electrical and Computer Engineering at Syracuse University
Doctor of Philosophy - PhD Computer Engineering, Doctor of Philosophy - PhD Computer Engineering at Northeastern University
Master of Science (MS) Engineering Technology General, Master of Science (MS) Engineering Technology General at Morehead State University
Bachelor of Engineering - BE Electrical Engineering, Bachelor of Engineering - BE Electrical Engineering at Guangxi University
English, Chinese