Kyle Jon Lafata

Thaddeus V. Samulski Associate Professor of Radiation Oncology

Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic 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 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.

Appointments and Affiliations

  • Thaddeus V. Samulski Associate Professor of Radiation Oncology
  • Associate Professor of Radiation Oncology
  • Associate Professor in Radiology
  • Assistant Professor of Pathology
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Member of the Duke Cancer Institute

Contact Information

  • Office Location: Radiation Physics, Box 3295 DUMC, Durham, NC 27710
  • Office Phone: +1 978 491 8730
  • Email Address:
  • Websites:


  • Ph.D. Duke University, 2018
  • C. Duke University, 2018
  • Duke University, School of Medicine, 2020

Research Interests

Biomedical imaging, image processing, computer vision, computational pathology, applied analysis of stochastic differential equations, high dimensional data analysis, feature engineering, adaptive imaging, multi-scale data fusion, Uncertainty Quantification

Courses Taught

  • MEDPHY 791: Independent Study in Medical Physics
  • MEDPHY 507: Radiation Biology
  • EGR 393: Research Projects in Engineering
  • ECE 899: Special Readings in Electrical Engineering
  • ECE 891: Internship
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 392: Projects in Electrical and Computer Engineering

Representative Publications

  • Stevens, Jack B., Breylon A. Riley, Jihyeon Je, Yuan Gao, Chunhao Wang, Yvonne M. Mowery, David M. Brizel, Fang-Fang Yin, Jian-Guo Liu, and Kyle J. Lafata. “Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.” In Med Phys, 51:3334–47, 2024.
  • Lafata, Kyle J., Charlotte Read, Betty C. Tong, Tomi Akinyemiju, Chunhao Wang, Marcelo Cerullo, and Tina D. Tailor. “Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population.” J Am Coll Radiol 21, no. 5 (May 2024): 767–77.
  • Zhao, Jingtong, Eugene Vaios, Yuqi Wang, Zhenyu Yang, Yunfeng Cui, Zachary J. Reitman, Kyle J. Lafata, et al. “Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.” Int J Radiat Oncol Biol Phys, April 12, 2024.
  • Yang, Zhenyu, Kyle Lafata, Eugene Vaios, Zongsheng Hu, Trey Mullikin, Fang-Fang Yin, and Chunhao Wang. “Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.” Med Phys 51, no. 3 (March 2024): 1931–43.
  • Riley, Breylon A., Jack B. Stevens, Xiang Li, Zhenyu Yang, Chunhao Wang, Yvonne M. Mowery, David M. Brizel, Fang-Fang Yin, and Kyle J. Lafata. “Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.” J Med Imaging (Bellingham) 11, no. 2 (March 2024): 024007.