The Huang lab focuses on biomedical technology development for high-resolution optical imaging and super-resolution microscopy/nanoscopy. The team collaborates closely with cell biologists, neuroscientists, and chemists to tackle fundamental biological questions in areas such as cytokinesis, epigenetics, neural circuits, and cell motility.
We develop novel imaging instruments and analytical methods to visualize intra- and extra-cellular structures at the nanoscale in thick specimens such as tissues or small animals. In the past, the team developed novel Adaptive Optics and PSF engineering methods to super-resolve brain sections (Nature Methods, 15, 583-586, 2018), a deep learning framework for multiplexed single molecule analysis (Nature Methods, 15, 913-916, 2018), INSPR (in situ PSF retrieval) technology to expand the applicability of super-resolution imaging system from cells to tissues (Nature Methods, 17, 531-540, 2020) and most recently invented deep learning driven adaptive optics for deep tissue (up to 250 µm-cut tissue) super-resolution imaging (Nature Methods, 20, 1748–58, 2023).
The team seeks to develop unconventional ultra-high resolution systems that synergistically combine ideas from engineering, physics, and mathematics such as interferometric/4Pi single molecule detection (Cell, 166, 4, 1028-1040, 2016), applied statistics (Nature Methods, 10, 653-658, 2013; Nature Methods, 14, 760-761, 2017) and coherent pupil function (Communications Biology, 3, 220, 2020) to significantly advance the achievable resolution limit for fixed and living specimens.
Our most recent work on optical theory provided an update to Abbe’s diffraction limit, an 150-year old theory, by incorporating photon-statistics into resolution limit calculation, namely an information-based resolution limit for finite photons (Nature Communications, 15, 3760, 2024).
2025 fall positions are no longer available, but we are recruiting PhD students for 2026 Fall admission!
We develop novel imaging instruments and analytical methods to visualize intra- and extra-cellular structures at the nanoscale in thick specimens such as tissues or small animals. In the past, the team developed novel Adaptive Optics and PSF engineering methods to super-resolve brain sections (Nature Methods, 15, 583-586, 2018), a deep learning framework for multiplexed single molecule analysis (Nature Methods, 15, 913-916, 2018), INSPR (in situ PSF retrieval) technology to expand the applicability of super-resolution imaging system from cells to tissues (Nature Methods, 17, 531-540, 2020) and most recently invented deep learning driven adaptive optics for deep tissue (up to 250 µm-cut tissue) super-resolution imaging (Nature Methods, 20, 1748–58, 2023).
The team seeks to develop unconventional ultra-high resolution systems that synergistically combine ideas from engineering, physics, and mathematics such as interferometric/4Pi single molecule detection (Cell, 166, 4, 1028-1040, 2016), applied statistics (Nature Methods, 10, 653-658, 2013; Nature Methods, 14, 760-761, 2017) and coherent pupil function (Communications Biology, 3, 220, 2020) to significantly advance the achievable resolution limit for fixed and living specimens.
Our most recent work on optical theory provided an update to Abbe’s diffraction limit, an 150-year old theory, by incorporating photon-statistics into resolution limit calculation, namely an information-based resolution limit for finite photons (Nature Communications, 15, 3760, 2024).
2025 fall positions are no longer available, but we are recruiting PhD students for 2026 Fall admission!