2020    Washington University in St. Louis，Electrical Engineering     Ph.D.

2016    Washington University in St. Louis，Electrical Engineering    M.S.

2014    Queen Mary University of London，Telecommunications Engineering with Management    B.S.

2014    Beijing University of Posts and Telecommunications，Telecommunications Engineering with Management    B.S.

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

2022 - Pres.    UM-SJTU Joint Institute，SJTU    Assistant Professor

2020 - 2022    University of California，Santa Barbara，Department of Bioengineering    Postdoctoral Scholar

2019 - 2020    University of Illinois at Urbana-Champaign，Department of Bioengineering    Visiting Scholar

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

• Imaging science

• Image reconstruction

• Visual perception

• Applied artificial intelligence and machine learning for medical imaging

• 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.