Shujun Wang

Dr. Emma Shujun WANG is an Assistant Professor at PolyU BME.  Before that, she was a Research Associate in the Department of Applied Mathematics and Theoretical Physics, at the University of Cambridge, from 2022 to 2023. She was a Postdoctoral Researcher in the Department of Computer Science and Engineering at the Chinese University of Hong Kong, from 2021-2022. She received her Ph.D. from the Department of Computer Science and Engineering at the Chinese University of Hong Kong (CUHK) in 2021, and her B.Eng from Honors College at Northwestern Polytechnical University (NPU) in 2017.

Her research is in the interdisciplinary field of artificial intelligence (AI) and healthcare. More specifically, she specializes in the areas of machine learning (ML), deep learning (DL), and their major applications in digital health and computational precision medicine. She is dedicated to designing AI-driven computational methods to enable reliable decision-making models for precision medicine, covering disease diagnosis to prognosis, and from medical image computing to multi-modal biomedical data integration. Her current and future research will facilitate personalized prognosis and treatment with multi-modal biomedical data computing from both imaging and non-imaging information and reliable machine learning-based disease diagnosis algorithms.

She has published 20 papers in top-tier conferences and journals (on The Lancet Digital Health (IF: 36.615), IEEE-TMI, MedIA, NeurIPS, AAAI, ECCV, MICCAI, etc.). Her team won the Best Paper Award for the CMMCA workshop at MICCAI 2022. She has attended several International challenges on medical image analysis and got the Champion of the REFUGE challenge in 2018 and Second place in the PALM challenge in 2019. She also co-organized the workshop of Women in Medical Image Understanding and Analysis in 2022. What’s more, she serves as a reviewer for top-tier journals and conferences. And she got IEEE TMI Distinguished Reviewer this year.

Medical Image Analysis with Deep Learning, AI in Digital Health