Summary
Melissa Pan is a PhD student and Graduate Research Assistant at UC Berkeley with eight years of software engineering experience spanning backend systems, ML-enabled edge compilers, and research prototyping. She has shipped performance-critical C++ features at IBM for Db2 recovery systems, built GPU collective optimizations for LLM training at Google, and contributed end-to-end to Edge TPU and CoreML TensorFlow tooling as an intern. Melissa combines rigorous academic training (Carnegie Mellon M.Eng. with a 4.0 GPA) and hands-on research in assistive ML—publishing work on wearable face-recognition aids—bridging human-centered design and scalable systems. Based in Berkeley, she is equally comfortable modernizing legacy test and deployment workflows as designing novel ML pipelines, and her GitHub ethos—"Today you are you that is truer than true"—hints at a pragmatic, user-focused approach to engineering.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Berkeley
Bachelor of Applied Science - BASc, Computer Engineering, Graduated with Honour, Bachelor of Applied Science - BASc, Computer Engineering, Graduated with Honour at University of Toronto
Master of Electrical and Computer Engineering – Applied Study, Electrical and Computer Engineering, 4.0/4.0, Master of Electrical and Computer Engineering – Applied Study, Electrical and Computer Engineering, 4.0/4.0 at Carnegie Mellon University
English, Chinese, cantonese (chinese)