2016-2020，Washington University in St. Louis，Electrical Engineering，Doctor

2014-2016，Washington University in St. Louis，Electrical Engineering，Master

2014-2014，Queen Mary University of London，Telecommunications Engineering with Management，Bachelor

2014-2014，Beijing University of Posts and Telecommunications，Telecommunications Engineering with Management，Bachelor

2022-Pres.，Global Institute of Future Technology，SJTU，Assistant Professor

2020-2022，UM-SJTU Joint Institute，SJTU，Assistant Professor

2019-2020，University of California，Santa Barbara，Department of Bioengineering，Postdoctoral Scholar/Visiting Scholar

2016-2020，Washington University in St. Louis，Department of Biomedical Engineering，Research Assistant

2020，International Society of Optical Engineering Community Champion

2018，International Society of Optical Engineering Medical Imaging Conference Best Poster Award

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.

Introduction to Machine Learning（ECE4450J ）