2020    圣路易斯华盛顿大学，电气工程，博士

2016    圣路易斯华盛顿大学，电气工程，硕士

2014    伦敦玛丽女王大学，电信工程及管理，学士

2014    北京邮电大学，电信工程及管理，学士

2022-至今     上海交通大学溥渊未来技术学院    助理教授

2020-2022    加州大学圣巴巴拉分校心理与脑科学系    博士后

2019-2020    伊利诺伊大学厄巴纳香槟分校生物工程系    访问学者

2016-2020    圣路易斯华盛顿大学生物医学工程系    研究助理

• 影像科学

• 医学影像

• 视觉感知

• 机器学习

2023-2024，“双一流”校企合作课程（工程硕博士培养改革专项），医学成像原理，主持，负责人

2024-2026，上海交通大学交叉学科创新人才实践培养基地，眼科图像大数据诊疗创新基地，合作，合作者

• Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton, and M. A. Anastasio, “Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging,” Journal of Biomedical Optics, vol. 22, no. 4, p. 041015, 2017.
  

• W. Zhou, S. Bhadra, F. J. Brooks, H. Li, M. A. Anastasio, “Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks,” Journal of Medical Imaging, vol. 9, no. 1, p. 015503, 2022. (Featured Content)

• W. Zhou and M. P. Eckstein, “A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise,” in Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035. SPIE, 2022, pp. 60–67.

• K. Li, W. Zhou, H. Li, and M. A. Anastasio, “A hybrid approach for approximating the ideal observer for joint signal detection and estimation tasks by use of supervised learning and Markov-Chain Monte Carlo methods,” IEEE Transactions on Medical Imaging, vol. 41, no. 5, pp. 1114–1124, 2021.

• K. Li, W. Zhou, H. Li, and M. A. Anastasio, “Assessing the impact of deep neural network-based image denoising on binary signal detection tasks,” IEEE Transactions on Medical Imaging, vol. 40, no. 9, pp. 2295–2305, 2021.

• E. Y. Sidky, J. P. Phillips, W. Zhou, G. Ongie, J. P. Cruz-Bastida, I. S. Reiser, M. A. Anastasio, and X. Pan, “A signal detection model for quantifying overregularization in nonlinear image reconstruction,” Medical Physics, vol. 48, no. 10, pp. 6312–6323, 2021

• W. Zhou, H. Li, and M. A. Anastasio, “Approximating the ideal observer for joint signal detection and localization tasks by use of supervised learning methods,” IEEE Transactions on Medical Imaging, vol. 39, no. 12, pp. 3992–4000, 2020.

• W. Zhou and M. A. Anastasio, “Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks,” in Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, vol. 11316. International Society for Optics and Photonics, 2020, p. 113160D.

• Y. Chen, W. Zhou, C. K. Hagen, A. Olivo, and M. A. Anastasio, “Comparison of data-acquisition designs for single-shot edge-illumination x-ray phase-contrast tomography,” Optics Express, vol. 28, no. 1, pp. 1–19, 2020.

• W. Zhou, H. Li, and M. A. Anastasio, “Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods,” IEEE Transactions on Medical Imaging, vol. 38, no. 10, pp. 2456–2468, 2019.