Zhaoqiang Wang

I am a Ph.D. Candidate at University of California, Los Angeles, supervised by Prof. Liang Gao and Prof. Tzung Hsiai. I received my Bachelor of Engineering (BEng) degree in 2018 at Huazhong Univerisity of Science and Technology (HUST), Wuhan, China, mentored by Prof. Peng Fei.

My research focuses on computational imaging and high-speed 3D microscopes. I work on optical design and machine learning to address the transient volumetric biological process (e.g. blood blow, neural signals).

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03/2024   We posted the preprint of our high speed light field microscope for volumetric voltage imaging, finally! After years of iterations, the system becomes so much different than the original proposal at the beginning back in 2022. This long journey wouldn't be possible without my supportive collaborators.

10/2023   I finished my summer internship as Optical Scientist at Meta Reality Lab, Redmond, working on waveguide-based eye tracker and LCoS light engine for augmented reality (AR) glasses. Thanks to my mentors: Jian, Tiffany and Melissa.

12/2022   Our work on spectral encoding light field tomography (light field acquisition in 1D projections) has been accepted by Optica.

10/2022   I am honored to receive the Amazon Doctoral Student Fellowship 2022.

07/2021   We developed an imaging strategy to visualize the beating heart and intracardiac blood flow simultaneously in embryonic zebrafish. The work has been published on PLOS Computational Biology.

02/2021   Our work on deep learning enhanced light-field microscopy has been published on Nature Methods. If you check the manuscript Received Date, it says December, 2019.

09/2018   I started my Ph.D. training at UCLA.

We engineered the light field microscopy with dove prisms and anamorphic lens to exceed kilohertz volumetric frame rate for fluorescence detection. We demonstrated accurate tracking of blood cells in zebrafish larvae and detection of neural voltage spikes in leech ganglion.

We introduced in-plane radon transformation and spectral encoding to the light field photography, which can record sub-aperture images in the form of 1D projections. This allows for extremely compressive measurements of light field, high speed, light dataload using 1D sensors.

We proposed a pipeline to visualize, process and quantify the biomechanics in a beating embryonic zebrafish heart.

We trained a convolutional neural network to reconstruct 3D image stacks from raw measurements of light field microscope (LFM). It brings higher spatial resolution, less artifacts and higher processing throughput to LFM, without sacrificing the high speed.

Website template is from Jon Barron ✩