Seoul National Univ. DMSE
Notice

Seminar & Colloquium

Seminar & Colloquium
[세미나: 12월 15일(목), 오전 10시] KAIST 물리학과, 박용근 교수

[세미나: 12월 15일(목), 오전 10시]  KAIST 물리학과, 박용근 교수

 

Title

Holotomography and artificial intelligence: label-free 3D imaging, classification, and inference

 

Speaker

박용근 교수, Endowed Chair Professor, Department of Physics, KAIST

 

Education

- 2010  Ph.D., Medical Engineering and Medical Physics, Harvard-MIT Division of Health Sciences and Technology (advisors: Subra Suresh & Michael S. Feld)

- 2007  S.M., Mechanical Engineering, MIT (advisors: Subra Suresh & Michael S. Feld)

- 2004  B.S. Mechanical Engineering, Seoul National University (summa cum laude)

 

Professional Experience

- 2021.3 - 현재  Endowed Chair Professor, KAIST, Department of Physics

- 2020.3 - 현재  Professor, KAIST, Department of Physics

                     Adjunct Professor, Graduate School of Medical Science and Engineering

                     Adjuuct Professor, School of Electrical Engineering

- 2014.9 - 2020.2  Associate Professor, KAIST, Department of Physics 

- 2010.6 - 2014.8  Assistant Professor, KAIST, Department of Physics.

- 2015.12 - 현재  Director of Creative Research Center of Time-Reversal Mirror

- 2015.8 - 현재  Co-founder & CTO, Tomocube Inc.

- 2016.7 - 현재  Co-founder & Advisor, The.Wave.Talk Inc.

- 2018.9 - 2019.9  Visiting Professor, University of California, San Diego

- 2010.8  Visiting Scientist, MIT

 

| Date | Thursday, December 15th, 2022

| Time | 10:00 ~  

| Venue | 33동 330호

 

[Abstract]

Holotomography is a label-free high-resolution three-dimensional quantitative phase imaging (QPI) techniques. QPI uses refractive index (RI) distributions as intrinsic imaging contrast for label-free imaging. HT is optical analogous to X-ray computed tomography; multiple 2-D holograms of a sample are measured with various illumination angles, from which a 3-D RI distribution of the sample is reconstructed by inversely solving the wave equation. 

 

When label-free and quantitative 3D imaging capability of HT is combined with machine learning approaches, it can provide synergistic capability in bioimaging and clinical diagnosis. We will discuss the potentials and challenges of combining QPI and artificial intelligence in terms of various aspects of imaging and analysis, including segmentation, classification, and imaging inference.

 

References: 

1. Y. Park, C. Depeursinge and G. Popescu, Nature Photonics 12 (10), 578-589 (2018).

2. Y. Baek and Y. Park, Nature Photonics 15 (5), 354-360 (2021).

3. S. Shin and Y. Park, Nature Materials, 2022

4. M. Lee, Y.-H. Lee, J. Song, G. Kim, Y. Jo, H. Min, C. H. Kim and Y. Park, Elife 9, e49023 (2020).

5. G. Kim, et al, Light: Science and Application, 2022

6. Y. Jo, H. Cho, W. S. Park, G. Kim, D. Ryu, Y. S. Kim, M. Lee, H. Joo, H. Jo, S. Lee, H.-S. Min and Y. Park, Nature Cell Biology (2022).

 

| Host | 도준상 교수 (880-1605)