• Hongming Shan
  • Research Direction:Machine learning, Medical Imaging, Radiomics, and Computer Vision
  • Email:hmshan@fudan.edu.cn
  • Website:http://hmshan.io/
  • Brief Introduction:Dr. Hongming Shan is a Young Principal Investigator at the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, and also a “Qiusuo” Research Leader at the Shanghai Center for Brain Science and Brain-inspired Technology. Before joining Fudan University in Sep. 2020, he worked with Prof. Ge Wang at the Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, where he was a Postdoctoral Research Associate from Apr. 2017 to May 2020, and a Research Scientist from Jun. 2020 to Aug. 2020. He received his Ph.D. in machine learning from Fudan University in Jan. 2017, under the supervision of Prof. Junping Zhang. He has published research papers in top journals including Nature Machine Intelligence, IEEE Transactions on Cybernetics, IEEE Transactions on Medical Imaging, IEEE Transactions on Information Forensics and Security, and Medical Image Analysis. His research has been covered by several media such as NIH, RPI News, EurekAlert!, Physics world, and HealthImaging.
  • Achievement:

    1.Shan,H.,Padole,A.,Homayounieh,F.,Kruger,U.,Khera,R.D.,Nitiwarangkul,C.,Kalra,M.K. and Wang,G.,2019. Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nature Machine Intelligence,1(6),pp.269-276.

    2.Shan,H.,Zhang,Y.,Yang,Q.,Kruger,U.,Kalra,M.K.,Sun,L.,Cong,W. and Wang,G.,2018. 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network. IEEE Transactions on Medical Imaging,37(6),pp.1522-1534.

    3.Shan,H.,Zhang,J. and Kruger,U.,2018. Framework of randomized distribution features for visual representation and categorization. IEEE Transactions on Cybernetics,49(9),pp.3599-3606.

    4.Shan,H.,Zhang,J. and Kruger,U.,2015. Learning linear representation of space partitioning trees based on unsupervised kernel dimension reduction. IEEE Transactions on Cybernetics,46(12),pp.3427-3438.

    5.Shan,H.*,Jia,X.*,Yan,P.,Li,Y.,Paganetti,H. and Wang,G.,2020. Synergizing medical imaging and radiotherapy with deep learning. Machine Learning: Science and Technology,1(2),021001.(* co-first authors)