물리학과

Current Research Interests

• Current Research Interests:
• I am a statistical physicist with an interdisciplinary background in polymer physics, soft matter, biophysics, and machine learning. My current research focuses on the theoretical modeling of polymeric materials and their nanostructures using polymer field theory and statistical mechanics. I also apply machine learning and deep learning to problems in soft matter and complex systems.
• 김재업 교수는 고분자물리, 연성물질물리, 생체물리, 기계학습 등 다양한 분야에서 연구 경험을 쌓아 온 통계물리학자이다. 현재 연구실에서는 고분자 장이론과 통계물리학을 바탕으로 고분자 물질의 특성과 이들이 형성하는 다양한 나노구조를 이론적으로 연구하고 있다. 이와 더불어 연성물질과 복잡계에서 나타나는 여러 현상을 통계물리학적 방법으로 탐구하며, 기계학습과 딥러닝을 연구에 적극적으로 활용하고 있다.
• Major research field
• Statistical Physics of Soft Matter, Polymer Physics, Biological Physics, Nanostructures & nanopatterning, Computational Physics, Non-equilibrium Statistical Physics, Machine Learning 연성물질의 통계물리학, 고분자물리학, 생물물리학, 나노구조&나노패터닝, 전산물리학, 비평형통계물리, 기계학습 및 딥러닝
• Research Keywords and Topics
• Polymer Theory 고분자 이론
• Block Copolymer Nanopatterning 블록공중합체 나노패터닝
• Statistical Physics & Computational Physics 통계물리/전산물리
• Soft Materials 연성물질
• Machine Learning & Deep Learning 기계학습 및 딥러닝

Biographical Sketch

• Curriculum Vitae
• • 2021 – present: Professor, UNIST
  • 2013 – 2020: Associate Professor, UNIST
  • 2009 – 2012: Assistant Professor, UNIST
  • 2006 – 2008: Postdoctoral Fellow, Physics & Mathematics Department, University of Reading, Reading, UK
  • 2005 – 2006: Postdoctoral Fellow, Columbia University, New York, Department of Chemical Engineering

• Academic Credential

• • 2005: M. A./M. Phil./Ph. D. in Physics Columbia University, New York
  • 1998: B. S. in Physics, KAIST

• Memberships

• • Member of American Physical Society
  • Member of Korean Physical Society
  • Member of Polymer Society of Korea
  • Member of Korean Chemical Society
  • Member of Korean Society for Baduk Studies

Selected Publications

• “ Dynamic Programming for Chain Propagator Computation of Branched Block Copolymers in Polymer Field Theory Simulations ”, Daeseong Yong* and Jaeup U. Kim; J. Chem. Theory Comput., 21, 3676–3690 (2025). See Publication
• “ Complex Implications of Translational Invariance in Polymer Field Theory ”, Jaeup U. Kim, Mark W. Matsen,* David C. Morse,* and James D. Willis; Macromolecules, 57, 20, 9900–9910 (2024). See Publication
• “ Characteristics of the Complex Saddle Point of Polymer Field Theory ”, Wonjun Kang, Daeseong Yong, and Jaeup U. Kim*; Macromolecules, 57, 8, 3850–3861 (2024). See Publication
• “ Atomically Flat, 2D Edge Directed Self-Assembly of Block Copolymers ”, Jang Hwan Kim, Hyeon U Jeong, Hye-In Yeom, Kyu Hyo Han, Geon Gug Yang, Hee jae Choi, Jong Min Kim, Sang-Hee Ko Park, Hyeong Min Jin*, Jaeup U. Kim*, Sang Ouk Kim*; Adv Mater., 35, 2207338 (2023). See Publication
• “ Accelerating Langevin Field-Theoretic Simulation of Polymers with Deep Learning ”, Daeseong Yong, Jaeup U. Kim*; Macromolecules, 55, 15, 6505–6515 (2022). See Publication

Lab Introduction video