Joel Saltz, MD, PhD
Distinguished Professor
Health Science Center, Level 3, Room 043
Stony Brook Medicine
Stony Brook, NY 11794-7025
Tel: (631) 444-2226 (Pathology)
Alt Tel: (631) 638-2590 (BMI)
Fax: (631) 444-3419
Email: Joel.Saltz@stonybrookmedicine.edu
Personal Statement:
Dr. Joel Saltz, MD, PhD, is a nationally recognized pioneer in pathology AI and digital Pathology. Dr. Saltz earned his MD, PhD in Computer Science through the NIH Medical Scientist Training Program at Duke University. He completed a Clinical Pathology residency and Pathology Informatics fellowship at Johns Hopkins, where he later served as Professor and Director of Pathology Informatics. Dr. Saltz is board‑certified in Clinical Pathology with subspecialty certification in Clinical Informatics. Dr. Saltz has been a member of the Pathology department at Ohio State University, Emory University and now Stony Brook University in addition to having been founding chair of Biomedical Informatics departments at all three universities.
Beginning over two decades ago, his teams at Ohio State and Emory helped demonstrate that machine learning could train algorithms to classify whole slide images and to recognize, classify, and extract salient features from cell nuclei and stromal microanatomical elements. Dr. Saltz led early machine learning Pathology studies at Ohio State that included neuroblastoma classification and computer‑assisted follicular lymphoma grading; both efforts employed pathology‑aware feature representations of nuclei and cytoplasm. At Emory, Dr. Saltz extended this early work to large‑scale nuclear morphology-based analysis in which nuclei were segmented, nuclear features extracted and clustered. These clusters were linked to GBM cancer genetics and were found to predict the rate of GBM progression. At Stony Brook, the Saltz group joined the TCGA Pan Cancer Atlas effort, generating maps of tumor infiltrating lymphocytes (TILs) across cancer types. Spatial features of these TIL maps were associated with molecular and outcome data in a comprehensive study published by the consortium in the journals Cancer Reports and Immunity
Dr. Joel Saltz's research established foundational methods for digital pathology by addressing data management and visualization challenges of whole-slide imaging (WSI). In the late 1990s, his group adapted high-performance computing techniques, originally developed for large-scale earth science applications, to create the "Virtual Microscope," the first published system for whole-slide viewing. This system was supported by two novel backend frameworks: the Active Data Repository (ADR), a MapReduce-style architecture for parallel computers, and DataCutter, a component-based system for distributed environments. These frameworks introduced key strategies for pathology imaging, including spatial partitioning of images into multi-resolution tile pyramids, parallel processing, and multi-resolution client-side caching.
At Stony Brook, Dr. Saltz targets pathology AI. Work includes generative histopathology. Saltz and his collaborators, Dimitris Samaras and Prateek Prasanna have introduced domain‑specific diffusion models for multi‑scale and text‑conditioned synthesis of realistic tissue images—PathLDM (text‑to‑histology), Learned Representation‑Guided Diffusion (conditioning on self‑supervised embeddings), and ZoomLDM (multi‑scale latent diffusion capable of producing globally coherent large images). These models are used to augment training data and are being used to impute IHC from H&E images. Jakub Kaczmarzyk, Saltz’s MSTP student jointly mentored with Peter Koo from Cold Spring Harbor Laboratory, has developed WSInfer, an open‑source framework that carries out efficient patch-based model inference. WSInfer has been integrated with QuPath for efficient whole‑slide predictions such as tumor and TIL maps. In collaboration with Dimitris Samaras, Gregory Zelinsky and Raj Gupta, Saltz is developing methods for self‑driving slide analysis. His group is building an autonomous “self‑driving pathology” framework that predicts pathologists’ attention and scan paths to identify diagnostically relevant regions and navigate whole‑slide images efficiently. Finally, the group is developing innovative methods to support interpretable AI and to develop highly optimized foundation pathology models.
Education:
1996–1999: Residency and Fellowship in Clinical Pathology and Pathology Informatics, Johns Hopkins Medical Institutions, Baltimore, MD
1985: MD, Duke University School of Medicine, Durham, NC
1985: PhD, Computer Science, Duke University, Durham, NC (Advisors: Thomas Gallie, Merrell Patrick)
1978: MS, Mathematics, University of Michigan, Ann Arbor, MI
1977: BS, Mathematics and Physics, University of Michigan, Ann Arbor, MI
Positions and Employment:
2020–present: SUNY Distinguished Professor, Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
2018–present: Co-Director, Institute for Engineering-Driven Medicine, Stony Brook University, Stony Brook, NY
2013–present: Vice President for Clinical Informatics, Stony Brook School of Medicine, Stony Brook University, Stony Brook, NY
2013–present: Cherith Professor and Founding Chair, Department of Biomedical Informatics, Clinical Appointment, Department of Pathology, Secondary Appointments: Departments of Computer Science, Applied Mathematics and Statistics, Radiology & Pathology, Stony Brook University, Stony Brook, NY
2021–2025: Vice Chair, Department of Pathology, Stony Brook University, Stony Brook, NY
2013–2019: Professor, Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
2011–2013: Professor and Chair, Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA
2008–2013: Professor, Department of Pathology and Laboratory Medicine, Department of Mathematics and Computer Science, Emory University College of Arts and Sciences, Professor of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University School of Medicine, Atlanta, GA
2008–2013: Chief Medical Information Officer, Emory Healthcare, Emory University School of Medicine, Atlanta, GA
2008–2013: Associate Vice President, Emory Healthcare, Emory University School of Medicine, Atlanta, GA
2008–2013: Adjunct Professor, School of Computer Science, Georgia Institute of Technology, Atlanta, GA
2004–2008: Endowed Chair, Dorothy M. Davis Cancer Fund, Arthur G. James Cancer Hospital, The Ohio State University, Columbus, OH
2001–2008: Professor and Chair, Department of Biomedical Informatics, College of Medicine, Professor, Department of Computer and Information Science, The Ohio State University, Columbus, OH
2002–2008: Vice Chair of Pathology and Director of Pathology Informatics, Department of Pathology, College of Medicine and Public Health, The Ohio State University, Columbus, OH
2001–2008: Associate Vice President for Health Sciences for Informatics, The Ohio State University Medical Center, The Ohio State University, Columbus, OH
2001–2002: Chief Information Officer, The Ohio State University, Columbus, OH
1999–2001: Professor, Division Head, Informatics, Associate Professor, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD
1999–2001: Director, Pathology Informatics, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD
1999–2001: Professor, Department of Computer Science and Institute for Advanced Computer Studies, Director, High Performance Systems Software Laboratory, University of Maryland, College Park, MD
1997–2005 — Thrust Area Lead, Programming Tools and Environments, National Partnership for Advanced Computational Infrastructure, San Diego Supercomputing Center
1992–1997: Associate Professor, Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, MD
1989–1992: Lead Computer Scientist, Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA
1986–1989: Assistant Professor, Department of Computer Science, Yale University, New Haven, CT
1985–1986: Staff Scientist, Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA
1978–1979: Mathematician, Science Applications, Inc., McLean, VA
Honors (selected):
2024: Member, National Academy of Inventors (NAI)
2024: Very Large Database (VLDB) Endowment, Test of Time Award
Awarded to a paper from the VLDB Conference (10–12 years prior) that demonstrates lasting impact, both in practice—such as adoption in products and services—and in academia through significant follow-up research.
Paper: Hadoop GIS: A High Performance Spatial Data Warehousing System over MapReduce
2020: American Association for Cancer Research (AACR) Team Science Award
The Cancer Genome Atlas (TCGA)
Recognized for extensive collaborations in compiling the largest-ever set of tumor characterization data, enabling new avenues of research to improve prevention, diagnosis, and treatment of various cancers.
2019: SUNY Distinguished Professorship
2017: AMIA Marco Ramoni Distinguished Paper Award
Paper: Towards Generation, Management and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research
2010: Distinguished Cancer Scholar, Georgia Cancer Coalition
2008: Fellow, American College of Medical Informatics (ACMI)
2008: Eminent Scholar, Georgia Research Alliance
2008: IEEE IPDPS Charles Babbage Award — for notable contributions to the field of bioinformatics grids
2002: Best Student Paper Award, Supercomputing — Active Proxy-G
2000: Best Student Paper Award, Sixth International Symposium on High Performance Computer Architecture — Active Disks: Programming Model, Algorithms and Evaluation
1998: Best Paper Award, American Medical Informatics Association (AMIA)
Selected Publications:
Latent Conditional Diffusion Methods : Controlled AI Generation of Synthetic Images
Yellapragada, S., Graikos, A., Triaridis, K., Prasanna, P., Gupta, R., Saltz, J. & Samaras, D. (2025). ZoomLDM: Latent Diffusion Model for Multi-scale Image Generation. IEEE/CVF} Conference on Computer Vision and Pattern Recognition {CVPR} 2025, Nashville, TN, USA, June 11-15, 2025, 23453--23463.
Graikos, A., Yellapragada, S., Minh-Quan, L. E., Kapse, S., Prasanna, P., Saltz, J. & Samaras, D. (2024). Learned Representation-Guided Diffusion Models for Large-Image Generation. {IEEE/CVF} Conference on Computer Vision and Pattern Recognition {CVPR} 2024, Seattle, WA, USA, June 16-22, 2024, 8532--8542. https://doi.org/10.1109/CVPR52733.2024.00815
Yellapragada, Srikar, Alexandros Graikos, Prateek Prasanna, Tahsin Kurc, Joel Saltz, and Dimitris Samaras. "PathLDM: Text conditioned latent diffusion model for histopathology." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 5182-5191. 2024.
Virtual Pathologist – Towards Self-Driving Pathologist Models
Zelinsky, G., Chakraborty, S., Saltz, J., & Samaras, D. (2025). Predicting pathologist attention during cancer-image readings. Journal of Vision, 25(9), 2736-2736.
Pathology Foundation Model Methods
Kapse, S., Das, S., Zhang, J., Gupta, R. R., Saltz, J., Samaras, D., & Prasanna, P. (2024). Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning. Medical Image Analysis, 93, 103070. https://doi.org/10.1016/j.media.2023.103070
Tools for Efficient Patch Based Whole Slide Image Prediction – Integrated into QuPath
Kaczmarzyk, J. R., O’Callaghan, A., Inglis, F., Gat,S., Kurc,T., Gupta,R.,Bremer, E., Bankhead, P. & Saltz. J. "Open and reusable deep learning for pathology with WSInfer and QuPath." NPJ Precision Oncology 8, no. 1 (2024):.9. https://doi.org/10.1038/s41698-024-00499-9
VLDB Test of Time Award Paper
Wang, F., Rubao, L. E. E., Teng, D., Zhang, X. & Saltz, J. (2024). High-Performance Spatial Data Analytics: Systematic R\&D for Scale-Out and Scale-Up Solutions from the Past to Now. Proc. {VLDB} Endow., 17(12), 4507--4520. https://doi.org/10.14778/3685800.3685912
Interpretable AI
Kapse S, Pati P, Das S, Zhang J, Chen C, Vakalopoulou M, Saltz J, Samaras D, Gupta RR, Prasanna P. (2024). SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology. IEEE/CVF} Conference on Computer Vision and Pattern Recognition {CVPR} 2024, Seattle, WA, USA, June 16-22, 2024, 11226--11237. https://doi.org/10.1109/CVPR52733.2024.01067
AI, Machine Learning and Digital Pathology:
Hou, L., Agarwal, A., Samaras, D., Kurc, T. M., Gupta, R. R., & Saltz, J. H. (2019). Robust histopathology image analysis: To label or to synthesize?. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8533-8542).
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., ... & Danilova, L. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell reports, 23(1), 181-193.
Hou, L., Samaras, D., Kurc, T. M., Gao, Y., Davis, J. E., & Saltz, J. H. (2016). Patch-based convolutional neural network for whole slide tissue image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2424-2433).
Sertel, O., Kong, J., Shimada, H., Catalyurek, U. V., Saltz, J. H., & Gurcan, M. N. (2009). Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development. Pattern recognition, 42(6), 1093-1103.
Sertel, O., Kong, J., Shimada, H., Catalyurek, U. V., Saltz, J. H., & Gurcan, M. N. (2009). Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development. Pattern recognition, 42(6), 1093-1103.
Pathology: Database, Imaging Viewing and Annotation
Pantanowitz, L., Sharma, A., Carter, A. B., Kurc, T., Sussman, A., & Saltz, J. (2018). Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. Journal of pathology informatics, 9(1), 40.
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., & Saltz, J. (2013). Hadoop GIS. Proceedings of the VLDB Endowment, 6(11), 1009-1020.
Catalyurek, U., Beynon, M. D., Chang, C., Kurc, T., Sussman, A., & Saltz, J. (2003). The virtual microscope. IEEE Transactions on Information Technology in Biomedicine, 7(4), 230-248.
Moon, B., Jagadish, H. V., Faloutsos, C., & Saltz, J. H. (2001). Analysis of the clustering properties of the Hilbert space-filling curve. IEEE Transactions on knowledge and data engineering, 13(1), 124-141.
Beynon, M. D., Kurc, T., Catalyurek, U., Chang, C., Sussman, A., & Saltz, J. (2001). Distributed processing of very large datasets with DataCutter. Parallel Computing, 27(11), 1457-1478.
Ferreira, R., Moon, B., Humphries, J., Sussman, A., Saltz, J., Miller, R., & Demarzo, A. (1997). The virtual microscope. In Proceedings of the AMIA Annual Fall Symposium (p. 449).
