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Lekang Yuan I am a first-year Ph.D. student in the ECE department at Princeton University, advised by Prof. Felix Heide. My research focuses on designing miniaturized computational imaging systems and exploring their transformative potential in a wide range of applications. Previously, I received my M.S. and B.S. degrees in Automation from Tsinghua University, where I developed ultra-compact microscopes capable of recording neural activity from thousands of neurons in freely moving mice under the supervision of Prof. Qionghai Dai. I also applied deep learning methods for rapid and reliable neural data analysis. |
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Research* indicates equal contribution. |
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A miniaturized mesoscope for the large-scale single-neuron-resolved imaging of neuronal activity in freely behaving mice
Yuanlong Zhang*, Lekang Yuan*, Qiyu Zhu*, Jiamin Wu, Tobias Nöbauer, Ruijin Zhang, Guihua Xiao, Mingrui Wang, Hao Xie, Zengcai Guo, Qionghai Dai & Alipasha Vaziri Nature Biomedical Engineering, 2024 paper / code A Systematically Optimized Miniaturized Microscope (SOMM) for studying neural populations in freely moving animals. By optimizing a diffractive optical element (DOE) under strict form factor constraints, SOMM achieves 4 µm resolution across a 3.6 × 3.6 mm² field of view and 300 µm depth of field, all within a lightweight 2.5-gram system. This design enables the recording of over 3,000 neurons with minimal impact on the animals' motion. |
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Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
Yuanlong Zhang*, Xiaofei Song*, Jiachen Xie*, Jing Hu*, Jiawei Chen, Xiang Li, Haiyu Zhang, Qiqun Zhou, Lekang Yuan, Chui Kong, Yibing Shen, Jiamin Wu, Lu Fang and Qionghai Dai. Nature Communication, 2024 paper / code This paper introduces a progressive optimization pipeline to optimize aspherical lenses and diffractive optical elements with significant memory reduction. The optimized microscope that outperforms a commercial 5×, NA 0.1 objective microscope while being only 0.15 cm³ and 0.5 g—five orders of magnitude smaller. The microscope is integrated into a cell phone for portable diagnostics, offering a novel approach to designing miniaturized, high-performance imaging systems. |
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DeSP: a systematic DNA storage error simulation pipeline
Lekang Yuan, Zhen Xie, Ye Wang & Xiaowo Wang BMC Bioinformatics, 2022 paper / code DNA is an appealing data storage medium in the era of data explosion due to its very high information density and longevity. This paper introduce DeSP, a systematic DNA data storage error Simulation Pipeline which covers all types of error generated in the data storage process. DeSP enables systemically optimization of the redundancy design in silico to combat the channel's particular noise structure. |
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