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Yide Zhang received his Ph.D. degree in Electrical Engineering from the University of Notre Dame and his B.Eng. in Automation from Huazhong University of Science and Technology. During his doctoral studies, Yide focused on advancing multiphoton fluorescence lifetime imaging microscopy and super-resolution microscopy, developing techniques that substantially reduced image generation time and cost. He also integrated machine learning into his research, creating methods that improved the accuracy and speed of fluorescence microscopy-based diagnostics. Additionally, he created a widely used Poisson-Gaussian denoising dataset, which has become a standard for benchmarking image denoising algorithms in fluorescence microscopy.

After completing his Ph.D., Yide joined Caltech as a postdoc, where he concentrated on pioneering photoacoustic imaging, quantum imaging, and ultrafast imaging techniques. His work in photoacoustic imaging enabled noninvasive, rapid imaging of hemodynamics, pushing the boundaries of medical imaging by overcoming limitations in depth and speed. In quantum imaging, he developed methods using entangled photons to address challenges in signal-to-noise ratios and resolution. Additionally, he visualized ultrafast phenomena, such as current flows in myelinated axons. Yide’s contributions have been recognized with several awards, including the Seno Medical Best Paper Award, Hitachi High-Tech Best Presentation Award, and the JenLab Young Investigator Award. His research is supported by the NIH K99/R00 Pathway to Independence Award, with a long-term goal of developing optical imaging technologies that advance translational research.