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Konstantinos Arfanakis 

Arfanakis specializes in neuroimaging. His research lab focuses on the development of quantitative magnetic resonance imaging (MRI) biomarkers of age-related neuropathologies of the brain using AI, as well as on the development of human brain atlases. His work is critical in identifying brain changes associated with age-related neuropathologies and dementias, such as Alzheimer’s and small vessel diseases, providing essential tools for the early diagnosis and tracking of disease progression.  

Gady Agam 

Agam’s research centers on computer vision and deep learning, with a focus on developing advanced computational methods for visual data analysis. His current application areas include medical imaging (such as brain MRI), diffraction X-ray imaging, perceptual computing, and AI for health care.  

Jovan Brankov 

Brankov develops next-generation health care technology by integrating novel imaging hardware, advanced computational methods, and task-based image quality assessment using human and deep-learning model observers. His work addresses core challenges in image formation and clinical performance. He serves as conference co-chair for the Society For Optics and Photonics’s (SPIE’s) Image Perception, Observer Performance, and Technology Assessment conferences, associate editor for the SPIE Journal of Medical Imaging and for IEEE Transactions on Biomedical Engineering and as a member of the task force drafting the International Commission on Radiation Units and Measurements (ICRU) report 38 on Model Observers in Medical Imaging.  

Yongyi Yang 

Yang is an expert in AI, image and signal processing, tomographic reconstruction, and computer-aided diagnosis (CAD). His research emphasizes the development of novel iterative reconstruction methods to improve the quality of emission tomographic images, particularly for cardiac and pediatric kidney imaging. He applies machine learning and deep learning algorithms to create sophisticated CAD systems for the early detection and classification of lesions, with a significant impact on breast cancer diagnosis in mammography.  

Kenneth Tichauer 

Tichauer is focused on advancing molecular and optical imaging for cancer diagnosis and improved therapy. His lab develops innovative imaging systems and protocols to better identify cancerous tissue during surgery. A key component of his work is quantitative paired-agent molecular imaging for predicting which therapies will be most effective for individual patients and detecting micro-metastases in lymph nodes.  

Keigo Kawaji 

Kawaji’s work spans cardiac MRI, neuroimaging, and novel quantitative tissue characterizations. He applies cutting-edge MRI data acquisition and AI techniques to cardiac MRI to automate and improve the assessment of heart disease. His research includes quantitative characterization of tissue-engineered constructs.  

Boris Gutman 

Gutman specializes in computational neuroimaging and imaging genetics. His work is based heavily on statistical learning and differential geometry. His lab develops AI and computational methods that fuse knowledge from cortical and subcortical anatomy, macroscopic brain connectivity, microscopic tissue properties and cellular dynamics to model the progression of neurological diseases such as Alzheimer's, Parkinson's, and schizophrenia. He also investigates the genetic effects on imaging phenotypes to crack the neuro-genetic code of brain disorders.