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Profile & Credentials
Academic background, research experience, projects, and selected talks.
AI / Optimization / Chemical Process Systems
Sunwoo Kim
Postdoctoral Researcher, Korea Institute of Energy Research (KIER)
I study optimization-compatible AI and mathematical programming for clean-energy systems, process systems, and decision making under uncertainty.
Daejeon, Republic of Korea - research experience across Korea, Europe, and North America
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Research Blog
Reviews of papers on AI, optimization, and decision-making tools, written for researchers and chemical engineers who want to understand modern computational methods.
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Better Judgment Notes
Reflections from books, interviews, and conversations on judgment, society, global affairs, and a better way to think.
Publication Record 논문 실적
Publications by year 연도별 논문 실적
17 journal articles, 3 conference proceedings, and 2 manuscripts under review. 학술지 논문 17편, 학회 프로시딩 3편, 심사 중 원고 2편을 정리했습니다.
Paper Notes & Code 논문 해설·코드
Published paper explanations and code 출판 논문 해설과 코드
A dedicated space for paper explanations and code. Content is coming soon. 논문별 해설과 코드를 위한 별도 공간입니다. 내용은 준비 중입니다.
Profile
Profile & Credentials
A compact map of academic background, research experience, projects, and selected talks. The full publication record is listed separately by year.
Publication record
Journal articles and conference proceedings by year, plus manuscripts under review.
Research experience
KIER, KAIST, USC, Waterloo, and related research experience.
Projects
Clean-energy, hydrogen, DAC, and process systems work.
Conferences
Selected talks and technical presentations.
Research
Research agenda
My work sits at the interface of process systems engineering, operations research, and machine learning. The common objective is to design decision-making methods that remain computationally tractable while respecting feasibility under uncertainty.
Clean-energy system planning
Long-horizon design and operation of green hydrogen, green ammonia, microgrid, offshore wind, and DAC-integrated systems under uncertain demand, renewable supply, cost, and policy conditions.
Stochastic optimization and decomposition
Multi-period and multi-timescale planning models that connect strategic capacity decisions with operational recourse, with emphasis on feasibility-critical decision making.
Learning-augmented optimization
Integration of reinforcement learning, Bayesian optimization, attention models, and optimization-compatible neural surrogates for scalable planning and control under uncertainty.
Featured research themes
Research Blog
Daily paper reading as research training
Paper reviews organized by application systems and algorithmic methods.
I review papers on decision-making tools based on modern AI and mathematical optimization in a way that chemical engineers can understand.
Just as elite athletes train every day, serious researchers read strong papers every day.
Better Judgment Notes
Better Judgment Notes
Reflections from books, interviews, and conversations on judgment, society, global affairs, and a better way to think.
For Wiser Decision-Making
Understanding Social Phenomena
Understanding Global Affairs
Experience
Experience
Projects
Selected projects and industrial exposure
Talks
Selected talks and conferences
Contact
For collaboration
I am interested in research collaborations and industry conversations on AI-enabled optimization, hydrogen and carbon-management systems, stochastic planning, and decision making under uncertainty.