Kyle J. Lafata, Thaddeus V. Samulski Associate Professor of Radiation Oncology

Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University with faculty appointments in Radiation Oncology, Radiology, Medical Physics, Electrical & Computer Engineering, and Mathematics. He joined the faculty at Duke in 2020 following postdoctoral training at the US Department of Veterans Affairs. His dissertation work 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 images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems).

Prof. Lafata has worked in various areas of computational medicine and biology, resulting in over 80 academic papers, 30 invited talks, and more than 100 national conference presentations. At Duke, the Lafata Laboratory focuses on the theory, development, and application of computational oncology. The lab interrogates disease at different length-scales of its biological organization via high-performance computing, multiscale modeling, advanced imaging technology, and the applied analysis of stochastic partial differential equations. Current research interests include tumor topology, cellular dynamics, tumor immune microenvironment, drivers of radiation resistance and immune dysregulation, molecular insight into tissue heterogeneity, and biologically-guided adaptative treatment strategies.

Contact Info:

Office Location:	Radiation Physics, Box 3295 DUMC, Durham, NC 27710
Email Address:
Web Page:	https://www.kylelafata.com

Education:

Ph.D.	Duke University	2018
C.	Duke University	2018

Recent Publications   (More Publications)

1. Mouheb, K; Nejad, MG; Dahal, L; Samei, E; Lafata, KJ; Segars, WP; Lo, JY, Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models, Lecture Notes in Computer Science, vol. 16171 LNCS (January, 2026), pp. 103-116 [doi]  [abs]
2. Xia, S-J; Vancoillie, L; Sotoudeh-Paima, S; Zarei, M; Ho, FC; Tushar, FI; Chen, X; Dahal, L; Lafata, KJ; Abadi, E; Lo, JY; Samei, E, Evaluation of unified harmonization of CT images across multiple tasks: A step towards AI generalizability., Med Phys, vol. 52 no. 11 (November, 2025), pp. e70084 [doi]  [abs]
3. Chisholm, M; Jabal, MS; He, H; Wang, Y; Kalisz, K; Lafata, KJ; Calabrese, E; Bashir, MR; Tailor, TD; Magudia, K, Investigating the Role of Area Deprivation Index in Observed Differences in CT-Based Body Composition by Race., J Am Coll Radiol, vol. 22 no. 10 (October, 2025), pp. 1182-1192 [doi]  [abs]
4. Stevens, JB; Je, J; Riley, BA; Mowery, YM; Brizel, DM; Liu, J-G; Wang, C; Lafata, KJ, Development and application of a novel tumor habitat analysis technique based on dynamical modeling., Med Phys, vol. 52 no. 9 (September, 2025), pp. e18032 [doi]  [abs]
5. Fan, F; Liu, Q; Zee, J; Ozeki, T; Demeke, D; Yang, Y; Bitzer, M; O'Connor, CL; Farris, AB; Wang, B; Shah, M; Jacobs, J; Mariani, L; Lafata, KJ; Rubin, J; Chen, Y; Holzman, LB; Hodgin, JB; Madabhushi, A; Barisoni, L; Janowczyk, A, Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis., Kidney Int, vol. 108 no. 2 (August, 2025), pp. 293-309 [doi]  [abs]

Recent Grant Support

• Development of a Virtual Preclinical CT Platform for Advanced Imaging and Theranostics in Head and Neck Cancer Research, National Institutes of Health, 2025/09-2029/08.      
• Computational tumor phenotyping to interrogate treatment resistance and immune dysregulation in head and neck cancer, National Cancer Institute, 2024/06-2029/05.      
• Disparate Survival, Disparate Workforce: An Integrated Approach to Improving Head and Neck Cancer Outcomes and Diversity in the Oncology Workforce, National Institute of Dental and Craniofacial Research, 2024/04-2029/01.      
• Targeting the B Cell Response to Treat Antibody-Mediated Rejection, National Institute of Allergy and Infectious Diseases, 2021/08-2028/05.      
• Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications, National Cancer Institute, 2022/09-2027/08.      
• Computer-Aided Triage of Body CT Scans with Deep Learning, National Cancer Institute, 2025/08-2027/07.      
• Computational Pathology of Proteinuric Diseases (R01), National Institute of Diabetes and Digestive and Kidney Diseases, 2018/10-2026/07.      
• Multi-scale characterization of radiation resistance in head and neck squamous cell carcinoma , Department of Defense, 2021/08-2025/07.