Wang, Ge

Rensselaer Polytechnic Institute, USA

Ge Wang (Fellow, IEEE, SPIE, AAPM, OSA, AIMBE, AAAS, and NAI) is the Clark & Crossan Endowed Chair Professor and Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute (Troy, New York, USA). He pioneered the cone-beam spiral CT method in 1991 and published the first perspective on AI-based tomographic imaging in 2016. His other notable contributions, in collaboration with his peers, include interior tomography, bioluminescence tomography, and innovative photon-counting CT algorithms. Dr. Wang's interests encompass AI-based imaging, teaching, and publishing. His recent honors include the 2021 IEEE EMBS Career Achievement Award, 2022 SPIE Meinel Technology Award, 2022 Sigma Xi Chubb Award for Innovation, 2023 RPI Wiley Distinguished Faculty Award, 2023 IEEE R1 Outstanding Teaching Award, 2023 IEEE NPSS/NMISC Hoffman Medical Imaging Scientist Award, and 2024 IEEE TRPMS Best Paper Award. A dedicated supporter of IEEE TMI throughout his career, Dr. Wang, as the Editor-in-Chief, is committed to upholding TMI's tradition of excellence and advancing the AI4TMI initiative in collaboration with the global medical imaging community.

Kruger, Uwe

Rensselaer Polytechnic Institute, USA

Uwe Kruger (Senior Member, IEEE) is a Professor of Practice in Biomedical Engineering at Rensselaer Polytechnic Institute (Troy, New York, USA). From the beginning his research career in 1996, he has been developing innovative methods related to multivariate statistics and machine learning for data mining of operational data from complex processes. His most notable contributions include the development of monitoring statistics for serially correlated data and creating new univariate statistics to enhance sensitivity to anomalous signal contributions for detection and diagnosis. Currently, he coordinates the Biomedical Data Science M.Eng. program and is a passionate educator in probability and statistics, data science, machine learning, and artificial intelligence. Based on his research and teaching experience, he is the lead author of the monograph “Statistical Monitoring of Complex Multivariate Processes”, published in 2012, the lead author of the textbook “Modeling and Analysis of Uncertainty”, 2nd edition, published in 2023, and a co-author of the monograph “Foundations of Modern Artificial Intelligence - from Regression and Classification to Deep Learning and Beyond”, expected to be published in 2025. Since 2014, he has served as an associate editor for several major specialty journals. He is enthusiastic about leveraging his rich experience to support and advance TMI’s excellence including the AI initiative, AI4TMI.

Shan, Hongming

Fudan University, China

Hongming Shan (Senior Member, IEEE) is a Professor at the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University (Shanghai, China). Before joining Fudan University, he served as a Research Scientist under the mentorship of Prof. Ge Wang at the Biomedical Imaging Center, Rensselaer Polytechnic Institute (Troy, New York, USA). His research focuses on developing artificial intelligence techniques for medical image reconstruction and analysis, with a recent emphasis on multi-modal foundation models for medical imaging. He was honored with the Youth Outstanding Paper Award at the World Artificial Intelligence Conference in 2021. He is dedicated to advancing TMI’s management and impact through the AI4TMI initiative.