Ge Wang

Ge Wang is the Clark & Crossan Endowed Chair Professor and Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute. A pioneer in computed tomography and AI-based medical imaging, he developed the cone-beam spiral CT method and leads the 'AI4TMI' initiative as Editor-in-Chief of IEEE Transactions on Medical Imaging. His cross-disciplinary appointments span Biomedical Engineering, Electrical and Computer Engineering, and the Center for Computational Innovations.

Education:

• Ph.D. in Electrical and Computer Engineering, University at Buffalo
• M.S. in Remote Sensing, University of Chinese Academy of Sciences
• B.E. in Signal Processing, Xidian University

Research focuses on AI-driven tomographic reconstruction, low-dose CT, photon-counting systems, and multi-modality imaging integration. Key innovations include deep learning frameworks for image enhancement, virtual CT workflows, and foundation models for medical diagnostics. Recent work emphasizes generative AI for synthetic medical data creation and physics-inspired neural networks.

Publication analysis reveals three dominant themes: 1) Advanced reconstruction algorithms for low-dose CT, 2) Multimodal foundation models for cancer screening, 3) AI-optimized hardware interfaces. His 15 most recent articles demonstrate consistent focus on improving diagnostic accuracy while reducing radiation exposure.

Awards and Honors:

• IEEE EMBS Career Achievement Award (2021)
• SPIE Meinel Technology Award (2022)
• Sigma Xi Chubb Award for Innovation (2022)
• RPI Wiley Distinguished Faculty Award (2023)
• IEEE NPSS Hoffman Medical Imaging Scientist Award (2023)
• AAPM Edith H. Quimby Lifetime Achievement Award (2025)

Advises PhD students on photon-counting CT, MRI reconstruction, and AI applications. Secured over $40M in funding from NIH, NSF, GE Healthcare, and NVIDIA. Directs the Wang-AXIS Lab, collaborating with FDA, Stanford, and Harvard on projects like the NIH-funded cardiac CT initiative.

Leads the Biomedical Imaging Center's 25+ member team developing next-generation imaging systems. Current projects include real-time tomographic scanners, nanoparticle-enabled MRI, and hybrid CT-MRI platforms. The lab maintains open-source repositories for AI-based reconstruction algorithms.