Iksung KANG
{first name}.{last name} at berkeley.edu

I am currently working as a postdoctoral fellow at UC Berkeley, advised by Professor Na Ji and Stella X. Yu, and a recent PhD from MIT EECS.

At MIT, I was advised by Professor George Barbastathis and was a recipient of Korea Foundation for Advanced Studies scholarship throughout my doctoral study.

I am currently on the 2024-25 academic job market.

Email  /  CV  /  Google Scholar  /  Github

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Research

I am enthusiastic about applying machine learning to biomedicine through optical imaging. Collaboration and impact have been central to my research career. I have consistently worked closely with diverse teams of professionals to address important challenges in the field. Additionally, I prioritize ensuring that the developed methods and tools are accessible to general users. By upholding these values, I aim to contribute to developing and democratizing both hardware- and software-based tools for imaging and conducting high-impact research in biomedicine.

Preprints
Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields
Kang I, Kim H, Natan R, Zhang Q, Yu SX, Ji N
bioRxiv (2024) 2024.10.20.619284 https://doi.org/10.1101/2024.10.20.619284
Optical segmentation-based compressed readout of neuronal voltage dynamics
Kim S, Ko G, Kang I, Tian H, Fan LZ, Li Y, Cohen AE, Wu J, Dai Q, Choi MM
bioRxiv (2023) 2023.11.10.566599. https://doi.org/10.1101/2023.11.10.566599

Journal Publications
Coordinate-based neural representations for computational adaptive optics in widefield microscopy
Kang I*, Zhang Q*, Yu SX, Ji N
Nature Machine Intelligence (2024) 6, 714-725. https://doi.org/10.1038/s42256-024-00853-3
*Contributed equally and co-correspondence authors.
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
Kang I, Jiang Y, Holler M, Guizar-Sicairos M, Levi AFJ, Klug J, Vogt S, Barbastathis G
Optica (2023) 10(8), 1000-1008. https://doi.org/10.1364/OPTICA.492666
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Kang I*, Wu Z*, Jiang Y, Yao Y, Deng J, Klug J, Vogt S, Barbastathis G
Light: Science & Applications (2023) 12(131). https://www.nature.com/articles/s41377-023-01181-8
News: EurekAlert!
*Contributed equally.
Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)
Wu Z*, Kang I*, Yao Y, Jiang Y, Deng J, Klug J, Vogt S, Barbastathis G
eLight (2023) 3(7). https://doi.org/10.1186/s43593-022-00037-9
*Contributed equally.
Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network
Kang I*, de Cea M*, Xue J, Li Z, Barbastathis G, Ram R
Optica (2022) 9(10), 1149-1155. https://doi.org/10.1364/OPTICA.470712
*Contributed equally.
Dynamical machine learning volumetric reconstruction of objects’ interiors from limited angular views
Kang I*, Goy A, Barbastathis G
Light: Science & Applications (2021) 10(74). https://doi.org/10.1038/s41377-021-00512-x
News: EurekAlert!
*Correspondence author.
Recurrent neural network reveals transparent objects through scattering media
Kang I*, Pang S, Zhang Q, Fang N, Barbastathis G
Optics Express (2020) 29(4), 5316-5326. https://doi.org/10.1364/OE.412890
*Correspondence author.
Deep residual learning for low-order wavefront sensing in high-contrast imaging systems
Allan G*, Kang I*, Douglas E, Barbastathis G, Cahoy K
Optics Express (2020) 28(18), 26267-26283 (2020). https://doi.org/10.1364/OE.397790
*Contributed equally.
On the interplay between physical and content priors in deep learning for computational imaging
Deng M, Li S, Zhang Z, Kang I, Fang N, Barbastathis G
Optics Express (2020) 28(16), 24152-24170. https://doi.org/10.1364/OE.395204
Phase Extraction Neural Network (PhENN) with Coherent Modulation Imaging (CMI) for phase retrieval at low photon counts
Kang I*, Zhang F, Barbastathis G
Optics Express (2020) 28(15), 21578-21600. https://doi.org/10.1364/OE.397430
*Correspondence author.
Learning to synthesize: Robust phase retrieval at low photon counts
Deng M, Li S, Goy A, Kang I, Barbastathis G
Light: Science & Applications, 9(36) (2020). https://doi.org/10.1038/s41377-020-0267-2

Conference Proceedings & Presentations
Coordinate-based neural representations for computational adaptive optics in two-photon microscopy
Kang I*, Zhang Q, Yaeger C, Pham T, Yu SX, Harnett M, Ji N
SPIE Photonics West (2024) 12851-9 (28 January 2024). https://doi.org/10.1117/12.3008468
*Speaker, oral presentation.
On the use of deep learning for three-dimensional computational imaging
Barbastathis G, Pang S, Kang I*, Wu Z, Liu Z, Guo Z, Zhang F
SPIE Photonics West (2023) 124450J (8 March 2023). https://doi.org/10.1117/12.2655261
*Speaker, oral presentation.
Deep self-supervised learning for computational adaptive optics in widefield microscopy
Kang I*, Zhang Q, Ji N
SPIE Photonics West (2023) 123880H (16 March 2023). https://doi.org/10.1117/12.2658934
*Speaker, oral presentation.
Optical segmentation for compressed readout on sub-millisecond neuronal circuit dynamics – Diffractive Multisite Optical Segmentation Assisted Image Compression: DeMOSAIC
Kim S, Wu J, Kang I, Ko G, Tian H, Fan LZ, Li Y, Cohen AE, Dai Q, Choi MM
Frontiers in Neurophotonics (FiNs) (2022).
Photon-starved X-ray Ptychographic Imaging using Spatial Pyramid Atrous Convolution End-to-end Reconstruction (PtychoSPACER)
Wu Z, Kang I, Zhou T, Coykendall V, Ge B, Cherukara MJ, Barbastathis G
Computational Optical Sensing and Imaging (COSI) (2022) CF1D.6. https://doi.org/10.1364/COSI.2022.CF1D.6
Adaptive image segmentation for crosstalk-free high-speed compressive imaging
Kim S, Wu J, Kang I, Li Y, Tian H, Fan LZ, Cohen AE, Dai Q, Choi MM
Focus on Microscopy (FOM) (2022).
Three-dimensional reconstruction of integrated circuits by single-angle X-ray ptychography with machine learning
Kang I*, Yao Y, Deng J, Klug J, Vogt S, Honig S, Barbastathis G
Computational Optical Sensing and Imaging (COSI) (2021) CTu6A.4. https://doi.org/10.1364/COSI.2021.CTu6A.4
*Speaker, oral presentation.
Probability of error as an image metric for the assessment of tomographic reconstruction of dense-layered binary-phase objects
Kang I*, Barbastathis G
SPIE Photonics West (2021) 116530T (5 March 2021). https://doi.org/10.1117/12.2577264
*Speaker, oral presentation.
Deep neural networks to improve the dynamic range of Zernike phase-contrast wavefront sensing in high-contrast imaging systems
Allan G, Kang I, Douglas E, N'Diaye M, Barbastathis G, Cahoy K
SPIE Astronomical Telescopes + Instrumentation (2020) 1144349 (13 December 2020). https://doi.org/10.1117/12.2562927
A portable, low-cost, 3D-printed main magnetic field system for magnetic imaging
Kang I*
IEEE Engineering in Medicine and Biology Society (EMBS) (2017). https://doi.org/10.1109/EMBC.2017.8037619
*Speaker, oral presentation.

Patents
Adaptive optical correction in two-photon fluorescence microscopy with neural fields
Kang I, Ji N
U.S. Patent Application No. 63/707,628, filed October 15, 2024.
Teaching

Each classroom should foster an inclusive learning environment that embraces and respects the diverse backgrounds of students, while also providing equitable opportunities for learning and practice.

Kaufman Teaching Certificate Program (KTCP), Teaching & Learning Laboratory, Massachusetts Institute of Technology (2021)
- Workshop: I completed seven workshops to develop teaching skills as part of the teaching certificate program. A major part of the program involved introducing students to relevant research in teaching and learning and laying out future teaching models.
- Microteaching sessions: I also presented two microteaching sessions that were videotaped, where I received feedback on my performance regarding my teaching and provided feedback to other participants.
Teaching Assistant (2.16/2.168 Learning Machines), Department of Mechanical Engieering, Massachusetts Institute of Technology (2020)
I mentored course research projects, contributed to curriculum design, conducted after-hour office hours, and graded assignments. Class taught totaled around 40 students and comprised course research projects on the connection between machine learning and physical systems.
Mentoring

One's education becomes truly meaningful when they share their experiences with others who can benefit from them.

Course Project Mentor, Massachusetts Institute of Technology (2022)
- 2.C01/2.C51 Physical Systems Modeling and Design Using Machine Learning: I mentored a graduate student group for their end-term project on the image segmentation of noisy ultrasonic images.
- Mentored students: Anlage AM, Huang Y, Fayer I.
Course Project Mentor, Massachusetts Institute of Technology (2020)
- 2.16/2.168 Learning Machines: I mentored two graduate student groups for their end-term projects on (1) the reaction modeling to facilitate pharmaceutical process development using machine learning; and (2) the control of autonomous ocean vehicles using reinforcement learning.
- Mentored students: (1) Eyke NS, Russell BD, Lee RW-Y; and (2) Fountain TS, McGee WA, Cho HS, Edskes BK.
Volunteer, Korea Foundation for Advanced Studies Overseas Program (2018)
I participated as a volunteer in the Kingdom of Cambodia for a week, teaching children physics and building houses.

This website was updated on November 5th, 2024.
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