Summary
Jeongwhan Choi is a postdoctoral researcher at KAIST with eight years of experience in AI and applied machine learning, holding a Ph.D. from Yonsei University. His research spans graph neural networks, transformers, scientific machine learning, and time-series and climate modeling, with papers in ICML, ICLR, SIGIR and domain-specific venues. He has a strong track record in recommender systems and novel ODE/continuous-time approaches (e.g., reaction-diffusion and neural ODEs) applied to forecasting and collaborative filtering. Funded by South Korea’s National AI Research Lab, he bridges theoretical advances and practical systems, moving from undergraduate work on defect prediction and education tech to cutting-edge ML research. Jeongwhan combines a software-engineering undergraduate foundation and industry-relevant nanodegrees (self-driving cars, ML) with prolific academic outputs, reflecting both implementation skill and mathematical depth. Colleagues value his ability to translate complex differential-equation–based models into tangible solutions for traffic, climate, and recommendation problems.
8 years of coding experience
2 years of employment as a software developer
IBM Blockchain Foundation for Developers, IBM Blockchain Foundation for Developers at Coursera
Nanodegree, Self-Driving Car Engineering, Nanodegree, Self-Driving Car Engineering at Udacity
Combined MS/PhD Student, Artificial Intelligence, Combined MS/PhD Student, Artificial Intelligence at Yonsei University
Bachelor of Science - BS, Software Engineering, Magna Cum Laude, Bachelor of Science - BS, Software Engineering, Magna Cum Laude at Jeonbuk National University
English, Korean