Research

We develop intelligent imaging systems by co-designing optics, physics, computation, and AI. Our goal is to recover multidimensional, high-resolution information from challenging measurements, with an emphasis on non-destructive imaging and measurement.

01 · Microscopy

AI for Optical Microscopy and Adaptive Optics

We develop computational and AI-driven microscopy methods for imaging biological specimens under realistic experimental conditions, where optical aberrations, scattering, motion, and limited photon budgets degrade image quality.

Research topics

Computational & physical adaptive optics Neural-field-based aberration estimation & correction Volumetric microscopy Joint sample–aberration inference Physics-informed reconstruction

Computational adaptive optics for microscopy — Kang et al., *Nature Methods* (2026)

Selected publications

Kang et al., “Coordinate-based neural representations for computational adaptive optics in widefield microscopy,” Nature Machine Intelligence (2024)
Kang et al., “Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields,” Nature Methods (2026)

02 · Metrology

Computational Imaging for Metrology

We develop computational imaging methods for non-destructive, high-resolution characterization of semiconductor and industrial structures across optical and X-ray modalities.

Research topics

Computational X-ray tomography & ptychography Sparse-view 3D reconstruction Photon-limited imaging AI-assisted reconstruction Optical-to-X-ray translation

Computational X-ray imaging for semiconductor metrology — Kang et al., *Optica* 10, 1000–1008 (2023)

Selected publications

Kang et al., “Dynamical machine learning volumetric reconstruction of objects’ interiors from limited angular views,” Light: Science & Applications (2021)
Wu and Kang et al., “Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID),” eLight (2023)
Kang et al., “Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time,” Light: Science & Applications (2023)
Kang et al., “Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits,” Optica (2023)

03 · Imaging Systems

Computational Imaging Systems and Platforms

We co-design programmable optics, acquisition, calibration, and reconstruction as a single imaging system. These platforms enable new computational imaging principles to be tested physically and transferred across imaging modalities.

Research topics

Programmable computational imaging Ptychography & diffraction tomography Physics-aware acquisition & calibration Low-photon & limited-data imaging Adaptive measurement design GPU-accelerated inverse problems

Programmable computational imaging systems — Kang et al., *Optica* 9, 1149–1155 (2023)

Selected publications

Deng et al., “Learning to synthesize: Robust phase retrieval at low photon counts,” Light: Science & Applications (2020)
Kang et al., “Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts,” Optics Express (2020)
Kang et al., “Recurrent neural network reveals transparent objects through scattering media,” Optics Express (2021)
Kang et al., “Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network,” Optica (2023)