Dr. Amy Kuceyeski is an Associate Professor of Mathematics in the Department of Radiology at Weill Cornell Medicine and an Adjunct Associate Professor in Computational Biology and Statistics and Data Science at Cornell University. For over a decade, Amy has been interested in understanding how the human brain works in order to better diagnose, prognose and treat neurological disease and injury. Quantitative approaches, including machine learning, applied to data from rapidly evolving neuroimaging techniques, have the potential to enable ground-breaking discoveries about how the brain works. Amy has particular interest in non-invasive brain stimulation and pharmacological interventions, like psychedelics, that may be used to modulate brain activity and promote recovery from disease or injury. For more information, see her lab's website.
Dr. Mert Sabuncu is an Associate Professor in the Electrical and Computer Engineering Department at Cornell University and Cornell-Tech, with a secondary appointment in the Department of Radiology at Weill Cornell Medicine. His lab conducts research in the field of biomedical data analysis, in particular imaging data, and with an application emphasis on neuroscience and neurology. We use tools from signal/image processing, probabilistic modeling, statistical inference, computer vision, computational geometry, graph theory, and machine learning to develop algorithms that allow us to learn from and exploit large-scale biomedical data. For more information, see his website.
Dr. Qingyu Zhao is an Assistant Professor in the Department of Radiology at Weill Cornell Medicine, working at the intersection of machine learning and translational research in neuroimaging. His research focuses on machine-learning-based computational analysis of neuroimaging and neuropsychological data to explain brain–behavior relationships and identify biomedical phenotypes of neurological diseases. His work has been recognized with a K99 Pathway to Independence Award from the NIH, a NARSAD Young Investigator Award from the BBRF, and both the Innovator Grant Award and Chairman’s Award for Advancing Science from Stanford Psychiatry. Before joining Cornell, he received his PhD in Computer Science from UNC Chapel Hill and served as a faculty member in the Department of Psychiatry and Behavioral Sciences at Stanford School of Medicine. His broad interests lie in image analysis and statistical learning for the detection, diagnosis, and treatment of diseases. For more information, see his lab's website.
Dr. Johannes Paetzold is an Assistant Professor in the Department of Radiology at Weill Cornell Medicine. His research focuses on developing novel machine learning methods and solutions for computer vision and healthcare, with an emphasis on geometric and topological deep learning. He works on interpretable medical image analysis, leveraging graph neural networks, multimodal data integration, and generative models and actively engages in translational research, collaborating with clinical and biomedical experts to implement machine learning innovations in real-world medical settings. For more information, see his lab's website.
Dr. Leo Milecki is a Postdoctoral Associate at Weill Cornell Medicine, member of Dr. Zhao's lab. He is working on deep learning-based methodologies applied to neuroimaging data, focusing on better apprehending neurological developments and diseases. Previously, Leo received a PhD in Computer Science from Paris-Saclay University in France in January 2024. His PhD thesis focused on applying novel deep learning algorithms to analyze biomedical data toward graft rejection diagnostic or prognosis after renal transplantation, focusing on representation and un-/ weakly-/ self-supervised learning methodologies for multimodal and longitudinal data.