Xinge Yang

Xinge Yang (杨辛格)

I am a Ph.D. candidate at KAUST Computational Imaging Group, working with Prof. Wolfgang Heidrich. My research focuses on differentiable optical design and end-to-end imaging simulation. I explore the next generation computational cameras, sensing systems, and AR/VR glasses.

My representative work published in Nature Communications enables automated optical design. Based on this work, I maintain an open-source differentiable optical lens simulator DeepLens for end-to-end simulation and optimization of optics, sensor, and computer vision.

I am now on the job market for 2026 full-time positions. Please feel free to contact me if you know someone who is hiring.

CV / Email / Google Scholar / Github / LinkedIn / 知乎

Education

2022 - 2026 (expected): Ph.D. in Computer Science, KAUST, Saudi Arabia.
2020 - 2022: M.Sc. in Computer Science, KAUST, Saudi Arabia.
2016 - 2020: B.Sc. in Physics (major) and Computer Science (minor), USTC, China.

Working

07/2024 - 11/2024: Research scientist Intern, XR Tech Camera & Sensing, Meta Reality Labs, Sunnyvale, CA, USA.
10/2023 - 01/2024: Research scientist Intern, Optics & Display Research, Meta Reality Labs Research, Redmond, WA, USA.

Research

First author papers:
	End-to-end differentiable design of geometric waveguide displays Xinge Yang, Zhaocheng Liu, Zhaoyu Nie, Qingyuan Fan, Jim Bonar, Wolfgang Heidrich arXiv preprint 2026. Paper (arXiv) Differentiable Monte Carlo non-sequential polarization ray tracing for geometric waveguide display coating optimization up to thousand layers.
	Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur Xinge Yang, Chuong Nguyen, Wenbin Wang, Kaizhang Kang, Wolfgang Heidrich, Ginger Li ICCV Workshop 2025. Paper (CVF) / Paper (PDF) / Supp (PDF) Efficient and high-fidelity synthetic dataset generation for denoising and defocus deblur without fine-tuning.
	End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model Xinge Yang, Matheus Souza, Kunyi Wang, Praneeth Chakravarthula, Qiang Fu, Wolfgang Heidrich Siggraph Asia 2024. Paper (ACM) / Paper (PDF) / Supp (PDF) Differentiable ray-wave optical model for hybrid refractive-diffractive lens design with two camera prototypes (extended depth of field and wide field of view).
	Task-Driven Lens Design for Image Classification Xinge Yang, Qiang Fu, Yunfeng Nie, Wolfgang Heidrich arXiv preprint 2023. Paper (arXiv) / Paper (PDF) / Supp (PDF) Task-driven lens design objective using a well-trained network to improve computer vision performance (image classification, object detection, and vision-language models).
	Aberration-Aware Depth-from-Focus Xinge Yang, Qiang Fu, Mohamed Elhoseiny, Wolfgang Heidrich TPAMI & ICCP 2023. Paper (IEEE) / Paper (PDF) / Supp (PDF) / Project page / Code Efficient and high-fidelity synthetic dataset generation with neural PSF representation for depth estimation from defocus stacks.
	Curriculum Learning for ab initio Deep Learned Refractive Optics Xinge Yang, Qiang Fu, Wolfgang Heidrich Nature Communications 2024. Paper (Nature) / Paper (PDF) / Supp (PDF) / Code / Video Automated lens design from scratch with differentiable ray tracing and curriculum-based deep learning optimization.
Co-author papers:
	Tolerance-Aware Deep Optics Jun Dai, Liqun Chen, Xinge Yang, Yuyao Hu, Jinwei Gu, Tianfan Xue arXiv preprint 2025. Paper (arXiv) Project page Considering lens manufacturing tolerances in end-to-end lens design for better optical and computer vision robustness.
	End-to-end Optimization of Fluidic Lenses Mulun Na, Hector Jimenez-Romero, Xinge Yang, Jonathan Klein, Dominik L. Michels, Wolfgang Heidrich Siggraph Asia 2024. Paper (PDF), Project page Differentiable optimization of liquid surface shapes and manufacturing for rapid prototyping.

Miscs

More about me: I like cats, photography, new techs, and most sports (especially basketball and water sports). I lived in China, Singapore, Saudi Arabia, and the US, and want to live in different places to experience the lifestyle of local people. I enjoy adventures and challenges. I love my life.
Peer review statement: I am pleased to be invited for peer review in my research field. I will try my best to complete the review within two weeks. I apply the same high standards to every paper, aiming for the utmost potential of the topic. However, I do not expect the authors to address every suggestion I make and leave the final judgment to the associate editor.

Last updated: 10/21/2025 The website template is from Dr. John Barron.