Personal data
Name & Surname: Q-Han Park
Business Address: Department of Physics; Korea University, Seoul, 02841, Korea.
Email: qpark@korea.ac.kr http://nol.korea.ac.kr/
Education
1978-1982, B.S. Physics Department, Seoul National University, Korea.
1983-1987, Ph.D. Physics Dept. Brandeis University, Waltham, MA, USA. Advisor L.F. Abbott
Postdoctoral experience
1990–1992: Postdoctoral Fellow, DAMTP, University of Cambridge, UK, with S. Hawking
1988–1990: Postdoctoral Fellow, Physics Department, University of Maryland, USA with J. Gates
1987–1988: Postdoctoral Fellow, Physics Department, Brandeis University, USA with H. Schnitzer
Professional experience
2004–present: Professor, Department of Physics, Korea University, Korea
2012–present: Consultant, Samsung Advanced Institute of Technology
2014–present: Director, Center for Electromagnetic Metamaterials, Korea University, Korea
2012–present: Fellow, the Korean Academy of Science and Technology
2012–2019: Associate Editor, Optics Express, OSA
2010–2012: Director, Research Institute Basic Sciences, Korea University, Korea
2001–2004: Associate Professor, Department of Physics, Korea University, Korea
1992–2001: Assistant/Associate Professor, Department of Physics, Kyunghee University, Korea
2011.1-2011.2: Visiting Faculty, Korea Institute for Advanced Study, Seoul, Korea.
2004.7-2005.2: Senior Visiting Fellow, Institute of Optics, Rochester, USA
2001/2002 summer: Visiting Associate Professor of Optics and of Physics and Astronomy
2000.1-2000.12: Senior Visiting Fellow, University of Rochester, Rochester, USA
1997,1999: Visiting Scholars, Aspen Center for Theoretical Physics, Aspen, USA
1996: Visiting Scholars, Center for Theoretical Physics, MIT, USA
1994/1995 summer: Research Associates, CERN, Geneve, Switzerland
Editorial service
Associate Editor, Optics Express, OSA
Fellowships, Awards
2023 Korea Univ. Alumni Award
2020 Korea Science Award by Ministry of Science and ICT.
2018 Best Research Award by Optical Society of Korea
2015 Fellow of the Optical Society of America
2012 Fellow of the Korean Academy of Science and Technology
2010 Sung-Do Optics Scientist Award by Optical Society of Korea
2002 Associate Fellow of the Korean Academy of Science and Technology
Funding organizations
National Research Foundation of Korea (NRF), Samsung Science and Technology Foundation
Selected publications
1. Universal impedance matching and the perfect transmission of white light, I. Ku, J.H.
Kang, Q-Han Park, Nature Photonics, 12, 143~149 (2018)
2. Terahertz field enhancement by a metallic nano slit operating beyond the skin-depth limit,
M.A.Seo et al. , Nature Photonics, 3, 152~156 (2009)
3. Local capacitor model for plasmonic electric field enhancement, J. H. Kang, D. S. Kim,
Q-Han Park, Phys. Rev. Lett. 102, 093906 (2009)
4. Coupling of surface plasmon polaritons and light in metallic nanoslits, K.G. Lee,
Q-Han Park, Phys. Rev. Lett. 95, 103902 (2005)
5. Chiral Light-Matter Interaction in Optical Resonators, SeokJae Yoo and Q-Han Park, Phys.
Rev. Lett. 114, 203003 (2015)
6. Microscopic Origin of Surface-enhanced Circular Dichroism, S. Lee, S.J. Yoo, Q-Han Park,
ACS Photonics, 4, 2047~2052 (2017)
7. Control of randomly scattered surface plasmon polaritons for multiple-input and
multiple-output plasmonic switching devices, W. Choi et al., Nature Comm. 8, 14636 (2017)
8. Imaging deep within a scattering medium using collective accumulation of single-scattered
waves, S.Kang et al. Nat. Photon. 9, 253 (2015)
9. Maximal energy transport through disordered media with the implementation of transmission
eigenchannels, M.S. Kim et al., Nature Photonics, 6, 583~587 (2012)
10. Enantioselective sensing by collective circular dichroism, Nature, 612, 470–476 (2022).
The recent surge of interest in AI, which was prompted by the launch of chat GPT, shows that
AI and machine learning are no longer specialized fields but rather aspects of everyday life.
Physics appears to be on the opposing side of machine learning based on massive data and
training, seeking natural principles defined by straightforward equations of motion giving
predictions without training. Nonetheless, these two fields are closely related and have
influenced one another's development.
We'll talk about ML's contributions to physics as well as physics' contributions to ML in
this session. We'll talk about ML's foundational ideas and how to apply them to physics research
and education. In particular, we describe how ML is integrated with cutting-edge mathematical
physics. We also look at recent efforts to integrate physics to machine learning itself.