Summary
Nontawat Charoenphakdee is a machine learning researcher with 11 years of experience blending deep theoretical work and practical engineering to build trustworthy, human-centric AI systems. Based at Preferred Networks, he leads R&D on industrial-scale ML for healthcare and quantum chemistry, contributing to high-impact publications including Nature Communications and PLOS Digital Health. His PhD from the University of Tokyo and a Google PhD Fellowship (2020) underscore strengths in algorithm analysis—particularly loss functions, evaluation metrics, weak supervision, and missing-value imputation—while his software engineering background ensures solutions are production-ready. He also serves on program committees and is a multiple outstanding reviewer awardee, reflecting a commitment to rigorous peer review and community service. Beyond papers, Nontawat has driven practical tools such as generative models for clinical data and uncertainty-aware ML potentials, demonstrating a rare mix of domain breadth and engineering delivery.
11 years of coding experience
4 years of employment as a software developer
University of Tokyo
Bachelor’s Degree, Computer Engineering, 3.80/4.00, Bachelor’s Degree, Computer Engineering, 3.80/4.00 at Chulalongkorn University (CU.)
High School Diploma, Science-mathematics Program, High School Diploma, Science-mathematics Program at Triam Udom Suksa Nomklao
English, Thai, Japanese