David Joon Ho is an Assistant Professor in the Department of Electrical and Computer Engineering at the State University of New York, Korea (SUNY Korea). His research focuses on developing artificial intelligence (AI) technologies that support clinical decision-making and ultimately improve patient care. Prior to joining SUNY Korea, Dr. Ho was an Assistant Professor at the National Cancer Center and an Instructor at Memorial Sloan Kettering Cancer Center, where he led multiple AI research projects in close collaboration with pathologists. He received his Ph.D. degree from Purdue University and his M.S. and B.S. degrees from the University of Illinois at Urbana-Champaign, all in Electrical and Computer Engineering.
Education
⦁ Ph.D. in Electrical and Computer Engineering, Purdue University, IN, USA (2019)
⦁ M.S. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, IL, USA (2012)
⦁ B.S. with Honors in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, IL, USA (2010)
Professional Experience
⦁ Assistant Professor, National Cancer Center, Korea (2023-2026)
⦁ Instructor, Memorial Sloan Kettering Cancer Center, NY, USA (2022-2023)
⦁ Machine Learning Scientist, Memorial Sloan Kettering Cancer Center, NY, USA (2019-2022)
⦁ Graduate Lecturer, Purdue University, IN, USA (2018)
Teaching Experience
⦁ Assistant Professor, National Cancer Center, Korea (2023-2026)
⦁ Graduate Lecturer, Purdue University, IN, USA (2018)
⦁ Graduate Teaching Assistant, Purdue University, IN, USA (2014-2018)
Ho Lab conducts interdisciplinary research at the intersection of artificial intelligence and medicine. Our mission is to develop AI technologies that improve patient care by providing quantitative analysis of medical images. Working closely with clinicians, we develop AI methods for cancer diagnosis, prognosis, and treatment response prediction, with a particular emphasis on the analysis of histopathology whole slide images. We are interested in building AI systems that translate advances in machine learning into real-world healthcare applications.
Research Areas
⦁ Deep Learning
⦁ Computer Vision
⦁ Medical Image Processing
Research Grants
⦁ Outstanding Young Scientist Grant, National Research Foundation of Korea (한국연구재단 신진연구(유형B)), March 2026 - February 2029, KRW 360M
Major Publications
⦁ D.J. Ho*, J.C. Chang*, R.G. Aly, H.C.T. Nguyen, P.S. Adusumilli, T.J. Fuchs, W.D. Travis#, C.M. Vanderbilt#. "Deep Learning–Based Segmentation of Lung Adenocarcinoma Whole-Slide Images for Objective Grading, Tumor Spread Through Air Spaces Identification, and Mutation Prediction." Modern Pathology, Vol. 38, No. 12, 100907, December 2025.
⦁ H.C.T. Nguyen and D.J. Ho. "fmMAP: A Framework Reducing Site-Bias Batch Effect from Foundation Models in Pathology." Proceedings of the Computational Pathology and Multimodal Data Workshop (COMPAYL) at the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), September 2025, Daejeon, Republic of Korea.
⦁ P. Khosravi, T.J. Fuchs, D.J. Ho. "Artificial Intelligence–Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality." Cancer Research, Vol. 85, No. 13, pp. 2356–2367, July 2025.
⦁ D.J. Ho, D.V.K. Yarlagadda, T.M. D'Alfonso, M.G. Hanna, A. Grabenstetter, P. Ntiamoah, E. Brogi, L.K. Tan, and T.J. Fuchs. "Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation." Computerized Medical Imaging and Graphics, Vol. 88, 101866, March 2021.
⦁ D.J. Ho*, N.P. Agaram*, P.J. Schüffler, C.M. Vanderbilt, M.-H. Jean, M.R. Hameed, and T.J. Fuchs. "Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment." Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 540–549, October 2020, Virtual.