Yuyao Xiao
DPhil Candidate
Oxford
Overview
5
5
years of professional experience
4
4
years of post-secondary education
Work History
DPhil Candidate
Department of Engineering Science
Oxford, Oxfordshire
10.2020 - Current
- Developed an AI-driven adaptive optics microscopy encapsulating the mathematics of Walsh mode lattices correcting complex shaped high-order aberrations, dealing with heterogeneous biological specimens.
- Significantly enhances image resolution and brightness by optimizing the performance of adaptive optics systems.
- Two-photon microscope design, construction, alignment, calibration, and operation.
- Microscope imaging and data analysis.
- Neural network construction, training, optimizing.
Education
Bachelor of Science - Opto-Electronics Information Science & Engineering
Beijing Institute of Technology
Beijing, China 08.2016 - 07.2020
Skills
- Expertise in MATLAB and Python programming
- Proficient in designing, training, and optimizing AI models
- Skilled in experimental design and execution
- Advanced data analysis using Fiji, MATLAB, and Python
- Experienced in microscope construction, calibration, and alignment
Publications
Yuyao, Xiao, et al."Two-Photon Adaptive Microscopy Using Walsh Mode Based Neural Network Aberration Correction." (Under construction)
Hu, Qi, et al. "The Lattice Geometry of Walsh-Function-Based Adaptive Optics." Photonics. Vol. 9. No. 8. MDPI, 2022.
Achievements
Presentation on 2025 Photonics West: Two Photon Adaptive Microscopy Using Walsh Mode Based Neural Network Aberration Estimation Existing adaptive optics microscopy methods correcting aberrations with complex high-order shapes commonly require a time-consuming optimization process. A neural network encapsulating the mathematics of Walsh mode lattices was built. Results suggested the method had the potential to outperform conventional methods. Furthermore, based on the aberration information extraction power of convolutional neural networks and the image pre-processing method called pseudo-PSF generation, a new image-based neural network was proposed, which will further improve the correction accuracy and robustness against noise and will shorten the convergence duration.
Timeline
DPhil Candidate
Department of Engineering Science
10.2020 - Current
Bachelor of Science - Opto-Electronics Information Science & Engineering
Beijing Institute of Technology
08.2016 - 07.2020
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