Summary
Taemin Heo is an assistant professor and computational scientist with 11 years of experience applying machine learning, reinforcement learning, and high-fidelity simulation to accelerate deployment of clean energy technologies. His work spans academia and national research institutes, including postdoctoral roles at MIT Energy Initiative and the Oden Institute, and focuses on predictive, efficient computational models for floating offshore wind, thermal storage, and climate-resilient infrastructure. Trained with a PhD in Civil Engineering from UT Austin and strong MS credentials from Seoul National University, he blends structural and geophysical domain knowledge with advanced data-science methods. He has a track record of translating research into practical decision tools—developing reliability-based life-cycle and near-future nonstationary climate models—helping prioritize which technologies to scale under tight resource and time constraints. Based in Seoul, he pairs interest in 3D graphics and ML with hands-on mentoring and teaching experience, having developed interactive R apps and supervised student research. Colleagues value his pragmatic focus on modeling efficiency and optimization to make net-zero strategies actionable rather than purely exploratory.
11 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Civil Engineering, 4.20/4.30, Master of Science - MS, Civil Engineering, 4.20/4.30 at Seoul National University
Doctor of Philosophy - PhD, Civil Engineering, 3.83/4.00, Doctor of Philosophy - PhD, Civil Engineering, 3.83/4.00 at The University of Texas at Austin