Dr. Yang Lei | Medical Physics | Best Researcher Award

Assistant Professor at Icahn School of Medicine at Mount Sinai, United States

Dr. Yang Lei is an Assistant Professor in the Department of Radiation Oncology at the Icahn School of Medicine at Mount Sinai and Adjunct Professor at the New York Proton Center. With a career spanning medical physics, AI applications in radiation therapy, and image-guided interventions, Dr. Lei has contributed extensively to the field through both academic and clinical avenues. He completed his postdoctoral research and medical physics residency at Emory University, where he also served as Chief Medical Physics Resident. His interdisciplinary expertise bridges therapeutic medical physics and advanced optimization algorithms for radiation treatment planning. Dr. Lei is an Associate Editor of Medical Physics and actively involved in professional communities such as AAPM, ASTRO, and the MR-Linac Consortium. Recognized with numerous awards and top-cited papers, Dr. Lei is a rising leader dedicated to advancing precision medicine and radiation oncology through innovative technology and collaborative science. 💡🔬

professional profile

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Education

Dr. Yang Lei’s academic path is defined by excellence in mathematical sciences and applied medical physics. He holds a Ph.D. in Information and Communication Engineering from Tianjin University (2016), where he developed face recognition algorithms using compressive sensing. He also earned an M.S. in Computational Mathematics (2013) and a B.S. in Mathematics & Computer Science (2010). From 2016 to 2021, he completed a postdoctoral fellowship at Emory University, focusing on AI-based image-guided radiation therapy. Dr. Lei further enhanced his clinical qualifications by completing a CAMPEP-accredited Medical Physics Certificate Program (2021–2022) and a Therapeutic Medical Physics Residency (2021–2024) at Emory University. His academic journey demonstrates a rare combination of mathematical theory, machine learning, and clinical innovation. This multidisciplinary education underpins his ability to drive impactful advancements in medical imaging, radiation therapy planning, and precision oncology. 🎓📘📊

Work Experience

Dr. Lei began his academic career as a postdoctoral fellow at Emory University in 2016, where he later became Chief Medical Physics Resident. He currently serves as Assistant Professor at Mount Sinai and Adjunct Professor at the New York Proton Center. His editorial contributions include serving as Associate Editor for Medical Physics, showcasing his leadership in peer review and publication ethics. Professionally, Dr. Lei has been deeply involved in advanced treatment planning, radiotherapy optimization, and AI-enhanced medical imaging. He is also an active member of several national and international working groups, such as the MR-Linac Consortium and AAPM’s Working Group on Student and Trainee Research. With experience in both research and clinical environments, Dr. Lei plays a crucial role in translating theoretical models into clinical applications. His trajectory reflects excellence in research, patient care, and professional service. 🏥🧠📈

Research Focus

Dr. Lei’s research integrates AI, optimization theory, and medical physics to enhance cancer treatment. His core focus is developing robust algorithms for inverse treatment planning, beam orientation optimization, and image-guided radiation therapy. He pioneers the use of large language models (LLMs) in auto-contouring and clinical decision support for brachytherapy and stereotactic body radiation therapy (SBRT). His past work includes sparse coding for face recognition, compressive sensing, and deep learning for medical image reconstruction. Dr. Lei’s research has evolved to cover non-convex optimization, second-order cone programming, and multi-objective treatment planning, significantly improving treatment precision. His projects emphasize translating computational models into clinical workflows to improve outcomes and reduce toxicity. His contributions advance the intersection of artificial intelligence, radiation oncology, and personalized medicine, setting new standards for automated and data-driven radiation therapy. 📊🤖⚕️

🏅 Awards and Honors

Dr. Lei has received numerous accolades for his academic contributions and clinical innovations. He is the recipient of a seed grant from Mount Sinai and a grant from Harvard Medical School’s Joint Center for Radiation Therapy. His research has earned recognition at multiple AAPM meetings, including the Science Council Research Award (2025) and top honors in Emory’s Annual Radiation Oncology Resident Research Day. Several of his papers have been named Wiley Top Cited and Downloaded Articles between 2020 and 2022, including first-author contributions in Medical Physics. His Editor’s Choice publication in Medical Physics and recognition as a Distinguished Reviewer in 2021 underscore his leadership in scientific communication. Dr. Lei also won Best Publication and Early-Career Investigator awards, solidifying his status as an emerging expert in radiation oncology and medical physics. 🥇📖🧪

🛠️ Skills

Dr. Lei brings a diverse and powerful skillset that spans computer science, applied mathematics, and clinical medical physics. He is proficient in machine learning frameworks (e.g., TensorFlow, PyTorch), optimization solvers (e.g., Gurobi, CVXPY), and programming languages including Python, MATLAB, and C++. His expertise covers sparse coding, compressed sensing, inverse planning, and second-order cone programming. Clinically, he is skilled in IMRT, VMAT, SBRT, HDR brachytherapy, and MR-guided radiation therapy. Dr. Lei is also adept in using treatment planning systems such as Eclipse, RayStation, and Monaco, and image processing platforms like 3D Slicer and MIM. He is experienced in deploying AI models in a medical physics workflow and integrating LLMs for auto-segmentation. His editorial, research, and mentoring experience further enhance his leadership in academic and professional settings. 💻🧠📐

Conclusion

Dr. Yang Lei is exceptionally qualified for the Research for Best Researcher Award. His scholarly productivity, clinical relevance, and technical innovation represent the highest standards of medical physics and data-driven oncology. His growing leadership in the research community, proven track record of publishing high-impact work, and commitment to education and mentoring make him a standout nominee. While future goals may include greater global visibility and expanded grant leadership, his current achievements already place him among the most promising and impactful researchers in his field. He is a compelling and deserving candidate for this honor.

Publications to Noted

Title: Deep learning in medical image registration: a review
Authors: Y Fu, Y Lei, T Wang, WJ Curran, T Liu, X Yang
Citations: 769
Year: 2020

Title: Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN
Authors: X Dong, Y Lei, T Wang, M Thomas, L Tang, WJ Curran, T Liu, X Yang
Citations: 361
Year: 2019

Title: MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks
Authors: Y Lei, J Harms, T Wang, Y Liu, HK Shu, AB Jani, WJ Curran, H Mao, T Liu, …
Citations: 359
Year: 2019

Title: Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography
Authors: J Harms, Y Lei, T Wang, R Zhang, J Zhou, X Tang, WJ Curran, T Liu, …
Citations: 314
Year: 2019

Title: A review on medical imaging synthesis using deep learning and its clinical applications
Authors: T Wang, Y Lei, Y Fu, JF Wynne, WJ Curran, T Liu, X Yang
Citations: 307
Year: 2021

Title: Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation
Authors: B Wang, Y Lei, S Tian, T Wang, Y Liu, P Patel, AB Jani, H Mao, WJ Curran, …
Citations: 273
Year: 2019

Title: A review of deep learning based methods for medical image multi-organ segmentation
Authors: Y Fu, Y Lei, T Wang, WJ Curran, T Liu, X Yang
Citations: 232
Year: 2021

Title: CBCT‐based synthetic CT generation using deep‐attention cycleGAN for pancreatic adaptive radiotherapy
Authors: Y Liu, Y Lei, T Wang, Y Fu, X Tang, WJ Curran, T Liu, P Patel, X Yang
Citations: 214
Year: 2020

Title: Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network
Authors: X Dong, Y Lei, S Tian, T Wang, P Patel, WJ Curran, AB Jani, T Liu, X Yang
Citations: 212
Year: 2019

Title: Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net
Authors: Y Lei, S Tian, X He, T Wang, B Wang, P Patel, AB Jani, H Mao, WJ Curran, …
Citations: 195
Year: 2019

Yang Lei | Medical Physics | Best Researcher Award