Kyle J. Lafata

Kyle J. Lafata is the Thaddeus V. Samulski Associate Professor at Duke University with faculty appointments spanning multiple departments including Radiation Oncology (primary), Radiology, Medical Physics, Electrical & Computer Engineering, and Mathematics. He joined the Duke faculty in 2020 following postdoctoral training at the US Department of Veterans Affairs. His interdisciplinary work bridges computational methods with clinical oncology applications.

Dr. Lafata completed his Ph.D. at Duke University in 2018. His dissertation focused on the applied analysis of stochastic partial differential equations and high-dimensional image phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate medical images.

Prof. Lafata's research centers on computational oncology, with particular expertise in tumor topology, cellular dynamics, and the tumor immune microenvironment. His work investigates drivers of radiation resistance and immune dysregulation, molecular insight into tissue heterogeneity, and biologically-guided adaptive treatment strategies. Through the Lafata Laboratory at Duke, he employs high-performance computing, multiscale modeling, and advanced imaging technology to interrogate disease at different length-scales of biological organization. His approach integrates stochastic partial differential equations with clinical applications to develop novel computational methods for cancer diagnosis and treatment.

Analysis of Prof. Lafata's recent publications reveals a strong focus on computational approaches to oncology and medical imaging. His work spans tumor habitat analysis, computational pathology of kidney diseases, lung cancer screening datasets, and digital twin technology. A recurring theme is the application of advanced computational methods—particularly radiomics, machine learning, and multiscale modeling—to extract meaningful biological insights from medical images. His research demonstrates increasing interdisciplinary collaboration across oncology, radiology, nephrology, and computational fields, with growing emphasis on health disparities and explainable AI in medical applications.

Prof. Lafata holds the Thaddeus V. Samulski Associate Professorship, an endowed position recognizing his contributions to the field. He has authored over 80 academic papers, delivered 30 invited talks, and presented more than 100 times at national conferences, demonstrating significant scholarly impact in computational medicine.

The Lafata Laboratory maintains active collaborations across Duke University and with external institutions. Current research is supported by multiple significant grants from the National Cancer Institute, National Institute of Dental and Craniofacial Research, National Institute of Allergy and Infectious Diseases, Department of Defense, and other funding agencies. These grants support work on computational tumor phenotyping, health disparities in head and neck cancer, B cell response in antibody-mediated rejection, breast calcification analysis, computational pathology of proteinuric diseases, and multi-scale characterization of radiation resistance.

Prof. Lafata teaches MEDPHY 717.01 (Techniques in Mathematical Oncology) and maintains an active research laboratory focused on developing and applying computational methods to solve challenging problems in oncology and medical imaging. His work represents a critical bridge between advanced computational techniques and clinical applications in cancer care.