Faculty Research Profile
수리과학과
성락경
부교수Rak-Kyeong Seong
- 연락처052-217-2683
- 이메일seong@unist.ac.kr
- 연구실Bldg. 108 Rm. 307, 401-14(Office)
Rak-Kyeong Seong
학력
- 2013: Ph.D. Theoretical Physics, Imperial College London, UK
- 2009: MASt (Part III) Mathematics, University of Cambridge, UK
- 2008: B.Sc. (Honours) Theoretical Physics, Imperial College London, UK
주요 경력
- 2024~present: Associate Professor, UNIST
- 2021~2024: Assistant Professor, Department of Mathematical Sciences, UNIST, Korea
- 2019~2021: Senior Researcher, Samsung SDS, Korea
- 2017~2019: Assistant Professor(Tenure-Track), Department of Mathematics and Yau Mathematical Sciences Center, Tsinghua University, China
- 2016~2017: Postdoctoral Research Fellow, Angstrom Laboratory, Uppsala University, Sweden
- 2013~2016: Postdoctoral Research Fellow, Korea Institute of Advanced Study, Korea
수상/학회/외부활동
- Samsung SDS Synergy Prize 2020
- Winton Ph.D. Prize, Imperial College London 2013
- Honorary Cambridge Overseas Trust Scholar, University of Cambridge
- Associate of the Royal College of Science (ARCS), UK
- Member, Korean Mathematical Society
- Member, Korean Physical Society
수리물리학 및 AI 연구실
Mathematical Physics and AI Lab
I am doing research at the interface of mathematics and theoretical physics, with a particular emphasis on the mathematical structures that arise in String Theory, Quantum Field Theory and supersymmetric gauge theories. I have also pioneered the introduction of machine learning tools in the study of moduli spaces of supersymmetric gauge theories realized in String Theory.
I am doing research at the interface of mathematics and theoretical physics, with a particular emphasis on the mathematical structures that arise in String Theory, Quantum Field Theory and supersymmetric gauge theories. I have also pioneered the introduction of machine learning tools in the study of moduli spaces of supersymmetric gauge theories realized in String Theory.
연구분야
1. 양자장이론 및 끈이론, 2. 수리물리학, 3. AI/머신러닝 / 1. Quantum Field Theory and String Theory, 2. Mathematical Physics, 3. AI/Machine Learning
1. Quantum Field Theory and String Theory, 2. Mathematical Physics, 3. AI/Machine Learning
연구주제
- 끈 이론, 양자장론, 수리물리학
- AI/머신 러닝의 응용: 딥 러닝, 강화 학습
- 응용수학: 최적화, 전자 설계 자동화
- String Theory, Quantum Field Theory, Mathematical Physics
- Applications of AI/Machine Learning: Deep Learning, Reinforcement Learning
- String Theory, Quantum Field Theory, Mathematical Physics
- Applications of AI/Machine Learning: Deep Learning, Reinforcement Learning
국가연구개발사업 기술 분류체계
국가과학기술표준분류
NB. 물리학 > NB01. 입자·장물리 > NB0102. 장이론/끈이론/양자중력이론
논문
[Note: authors are listed alphabetically]
1. COMMUNICATIONS IN MATHEMATICAL PHYSICS / Calabi-Yau Volumes and Reflexive Polytopes / Yang-Hui He, Rak-Kyeong Seong (corresponding author), Shing-Tung Yau / 2018-04
2. PHYSICAL REVIEW D / Machine Learning of Calabi-Yau Volumes / Daniel Krefl, Rak-Kyeong Seong (corresponding author) / 2017-09
3. JOURNAL OF HIGH-ENERGY PHYSICS / Quadrality for Supersymmetric Matrix Models / Sebastian Franco, Sangmin Lee, Rak-Kyeong Seong (corresponding author), Cumrun Vafa / 2017-07
4. JOURNAL OF HIGH-ENERGY PHYSICS / Fano 3-Folds, Reflexive Polytopes and Brane Brick Models / Sebastian Franco, Rak-Kyeong Seong (corresponding author) / 2022-08
5. JOURNAL OF HIGH-ENERGY PHYSICS / Brane Brick Models for the Sasaki-Einstein 7-Manifolds Y^{p,k}(CP^1 x CP^1) and Y^{p,k}(CP^2) / Sebastian Franco, Dongwook Ghim, Rak-Kyeong Seong (corresponding author) / 2023-03