Tahsin Kurc

Tahsin Kurc headshot

Tahsin Kurc, Ph.D.

Research Associate Professor and Vice Chair
Department of Biomedical Informatics

HSC L3-044
Stony Brook University
Stony Brook, NY 11794
Phone: (631) 638-0038
tahsin.kurc@stonybrook.edu

Interests

Biomedical Informatics, Imaging Informatics, Clinical & Translational Research Informatics and High-End Computing

Biography

Tahsin Kurc received his Ph.D. in computer science from Bilkent University, Turkey and B.Sci. degree in electronics engineering from Middle East Technical University, Turkey. He joined the Biomedical Informatics Department in 2013. Before coming to Stony Brook, he was a faculty member in the Biomedical Informatics Department at Emory University.

Research

Dr. Kurc's research focuses on developing informatics platforms that enable scalable processing and analysis of large volumes of high-resolution, high-throughput imaging data on high-end computing systems, and that support the management, query, and exploration of scientific datasets. He has a strong research and software development background in computer science and biomedical informatics, specializing in databases and high-performance middleware systems for scientific applications. His work targets the requirements of basic and translational biomedical research projects that reference, analyze, and integrate large datasets. He has extensive experience collaborating with scientists and software developers on multidisciplinary projects, and has supervised postdoctoral researchers and software engineers in the design and implementation of integrated informatics platforms and high-performance computing software.

His early work addressed the computational and data management challenges of accessing and processing large scientific datasets on high-performance computing machines. This work showed how datasets can be organized on disk and across multiple storage nodes, and how data retrieval and processing can be coordinated to reduce overheads. The resulting techniques were implemented in the Virtual Microscope, one of the pioneering systems for viewing large whole slide tissue images, and in the Active Data Repository, a software system with a MapReduce-style processing structure. Both systems were successfully employed in scientific applications.

Through several collaborative projects, he extended this work to the design of software middleware systems and tools for the analysis, management, and exploration of imaging datasets and multi-dimensional scientific data. These extensions focused on data structures and runtime optimizations for rapidly executing image analysis workflows in distributed computing environments. More recently, he and his collaborators have adapted these techniques to make efficient use of hardware accelerators, such as graphics processing units (GPUs), for image analysis.

Alongside his work on data processing, Dr. Kurc has contributed to the development of tools and methods for the storage, management, and sharing of imaging and scientific datasets. Databases for biomedical imaging data must not only support rich data models to express image metadata and analysis results, but also scale to large volumes of image data, segmentation results, and image features. His work in this area showed how advanced database technologies, service-oriented architectures, and cloud computing can be used to manage datasets consisting of thousands of images and billions of features, and to support comparison of results from multiple analyses.

Building on these database and middleware systems, he has also participated in collaborative research that developed new image analysis techniques and applied them in biomedical research. This work has focused on the analysis of whole slide tissue images for in silico cancer research, demonstrating the potential of tissue morphology for classifying patients into molecular subgroups and studying the prognostic value of imaging features.

Selected Publications

  1. Schacherer DP, Bridge CP, Clunie D, Octaviano I, Homeyer A, Herrmann MD, et al. Sharing standardized image-derived data in computational pathology using DICOM. arXiv (Cornell University). 2026.
  2. Huang W, Xu M, Hu X, Abousamra S, Ganguly A, Kapse S, et al. RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency. IEEE Transactions on Medical Imaging. 2026;PP:1. doi:10.1109/tmi.2026.3725898 PMID: 42627750
  3. Zhang Y, Abousamra S, Hasan M, Torre-Healy LA, Krichevsky S, Shrestha S, et al. Pathomics tumor-infiltrating lymphocytes maps in colorectal cancer for translational immuno-oncology. Zenodo (CERN European Organization for Nuclear Research). 2026. doi:10.5281/zenodo.21985178
  4. Lowry NC, Breto A, Wallaengen V, Algohary A, Tapia-Stoll N, Gaston SM, et al. MRI-based patient selection for active surveillance in prostate cancer using U-Found: a generalized deep learning model. Cancer imaging : the official publication of the International Cancer Imaging Society. 2026;26(1):26. doi:10.1186/s40644-026-00988-z PMID: 41555464
  5. Abousamra S, Fassler D, Gupta R, Kurç T, Escobar‐Hoyos LF, Samaras D, et al. Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images. IEEE Transactions on Medical Imaging. 2026;45(2):853-864. doi:10.1109/tmi.2025.3609245 PMID: 40932806
  6. Ding Z, Han G, Ma T, Wang F, Hajagos J, Kurç T, et al. From National Data to Local Evaluation: Benchmarking Machine Learning Models for Perioperative Risk Prediction. medRxiv. 2026. doi:10.64898/2026.09.02.26361824
  7. Gaston SM, Zhang Y, Wallaengen V, Galvez A, Salcedo L, Breto A, et al. Abstract 691: Visualizing differential prostate cancer lesion growth in longitudinal MRIs of patients on active surveillance: Quantitative mapping by Habitat Risk Score and digital pathology. Cancer Research. 2026;86(7_Supplement):691. doi:10.1158/1538-7445.am2026-691
  8. Yellapragada S, Graikos A, Li Z, Triaridis K, Belagali V, Kapse S, et al. PixCell: A generative foundation model for digital histopathology images. ArXiv. 2025. PMID: 40503024
  9. Zhang Y, Abousamra S, Hasan M, Torre-Healy LA, Krichevsky S, Shrestha S, et al. Pathomics Image Analysis of Tumor Infiltrating Lymphocytes (TILs) in Colon Cancer. Research Square. 2025. doi:10.21203/rs.3.rs-6173056/v1 PMID: 40235501
  10. Podhorszki N, Eisenhauer G, Kurç T, Gong Q, Liu Q, Chen J, et al. HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP. 2025:91-95. doi:10.1145/3718350.3727199
  11. Gaston SM, Stoyanova RS, Kryvenko ON, Abramowitz MC, Breto A, Hernández EJ, et al. Early Detection of Prostate Cancer Immune Response to Radiation and Androgen Deprivation Therapy: Unleashing the Power of HRS Mpmri Imaging, Molecular Biomarkers and Digital Pathology. International Journal of Radiation Oncology*Biology*Physics. 2025;123(1):e609. doi:10.1016/j.ijrobp.2025.06.2880
  12. Zhang Y, Gupta R, Saltz JH, Kurç T, Kaczmarzyk J, Bremer E, et al. Abstract 5315: Mapping tumor infiltrating lymphocytes in whole prostatectomy specimens to visualize and quantify the immune microenvironment of prostate cancer. Cancer Research. 2025;85(8_Supplement_1):5315. doi:10.1158/1538-7445.am2025-5315
  13. Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, et al. Understanding metric-related pitfalls in image analysis validation. Nature Methods. 2024;21(2):182-194. doi:10.1038/s41592-023-02150-0 PMID: 36945687
  14. Yun J, Abousamra S, Chen L, Gupta R, Kurç T, Samaras D, et al. Uncertainty Estimation for Tumor Prediction with Unlabeled Data. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2024;2024:6946-6954. doi:10.1109/cvprw63382.2024.00688 PMID: 39553831
  15. Yellapragada S, Graikos A, Prasanna P, Kurç T, Saltz JH, Samaras D. PathLDM: Text conditioned Latent Diffusion Model for Histopathology. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2024;2024:5170-5179. doi:10.1109/wacv57701.2024.00510 PMID: 38808304
  16. Baid U, Pati S, Kurç T, Gupta R, Bremer E, Abousamra S, et al. Pan-Cancer Tumor Infiltrating Lymphocyte Detection based on Federated Learning. IEEE International Conference on Big Data (BigData). 2024:7640-7647. doi:10.1109/bigdata62323.2024.10825083
  17. Agraz JL, Agraz C, Chen AA, Rice C, Pozos RS, Aelterman S, et al. Optimized Whole-Slide-Image H&E Stain Normalization: A Step Towards Big Data Integration in Digital Pathology. IEEE Open Journal of Engineering in Medicine and Biology. 2024;6:35-40. doi:10.1109/ojemb.2024.3455011 PMID: 39564562
  18. Kaczmarzyk J, O’Callaghan A, Inglis F, Gat S, Kurç T, Gupta R, et al. Open and reusable deep learning for pathology with WSInfer and QuPath. npj Precision Oncology. 2024;8(1):9. doi:10.1038/s41698-024-00499-9 PMID: 38200147
  19. Maier‐Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, et al. Metrics reloaded: recommendations for image analysis validation. Nature Methods. 2024;21(2):195-212. doi:10.1038/s41592-023-02151-z PMID: 38347141
  20. Baghal A, Saltz JH, Kurç T, Prasanna P, Baghal S, Hajagos J, et al. Linking The Cancer Imaging Archive and GenBank to the National Clinical Cohort Collaborative. Learning Health Systems. 2024;9(1):e10457. doi:10.1002/lrh2.10457 PMID: 39822917
  21. Coka LD, Horowitz M, Gonçalves MT, Roa‐Peña L, Leiton CV, Hasan M, et al. Keratin 17 modulates the immune topography of pancreatic cancer. Journal of Translational Medicine. 2024;22(1):443. doi:10.1186/s12967-024-05252-1 PMID: 38730319
  22. Abel JT, Abraham TM, Arandjelović O, Balis UJ, Caie PD, Chauhan C, et al. Contributors. Elsevier eBooks. 2024:xv-xvii. doi:10.1016/b978-0-323-95359-7.09997-0
  23. Gupta R, Kurç T, Saltz JH. Artificial intelligence and the interplay between cancer and immunity. Elsevier eBooks. 2024:309-341. doi:10.1016/b978-0-323-95359-7.00015-7
  24. Foran DJ, Chen W, Kurç T, Gupta R, Kaczmarzyk J, Torre-Healy LA, et al. An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing. Cancer Informatics. 2024;23:11769351231223806. doi:10.1177/11769351231223806 PMID: 38322427
  25. Kaczmarzyk J, Torre-Healy LA, Moffitt RA, Gupta R, Hamilton AM, Kurç T, et al. Abstract B082: Early risk stratification of ER+/HER2- breast cancer patients using digital pathology and multi-task, weakly-supervised deep learning. Cancer Research. 2024;84(3_Supplement_1):B082. doi:10.1158/1538-7445.advbc23-b082
  26. Oblein L, Babu S, Gonçalves MT, Roa‐Peña L, Horowitz M, Hasan M, et al. Abstract B041: Keratin 17 excludes CD8-positive T cells and recruits CD163-positive macrophages in pancreatic ductal adenocarcinoma. Cancer Research. 2024;84(2_Supplement):B041. doi:10.1158/1538-7445.panca2023-b041
  27. Abousamra S, Fassler D, Yao J, Gupta R, Kurç T, Escobar‐Hoyos LF, et al. Unsupervised Stain Decomposition via Inversion Regulation for Multiplex Immunohistochemistry Images. PubMed. 2023;227:74-94. PMID: 38817539
  28. Abousamra S, Gupta R, Kurç T, Samaras D, Saltz JH, Chen C. Topology-Guided Multi-Class Cell Context Generation for Digital Pathology. IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings. 2023;2023:3323-3333. doi:10.1109/cvpr52729.2023.00324 PMID: 38741683
  29. Pati S, Thakur SP, Hamamcı İE, Baid U, Baheti B, Bhalerao M, et al. GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows. Communications Engineering. 2023;2(1). doi:10.1038/s44172-023-00066-3
  30. Meirelles ALS, Kurç T, Kong J, Ferreira R, Saltz JH, Teodoro G. Effective and efficient active learning for deep learning-based tissue image analysis. Bioinformatics. 2023;39(4). doi:10.1093/bioinformatics/btad138 PMID: 36943380
  31. Parashar M, Kurç T, Klíe H, Wheeler MF, Saltz JH, Jammoul M, et al. Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learned and Future Trends. Springer eBooks. 2023:287-330. doi:10.1007/978-3-031-27986-7_11
  32. Gupta R, Kurç T, Saltz JH. Data-Driven Cancer Research with Digital Microscopy and Pathomics. Springer eBooks. 2023:659-682. doi:10.1007/978-3-031-27986-7_24
  33. Kaczmarzyk J, Gupta R, Kurç T, Abousamra S, Saltz JH, Koo PK. ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification. Computer Methods and Programs in Biomedicine. 2023;239:107631. doi:10.1016/j.cmpb.2023.107631 PMID: 37271050
  34. Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, et al. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nature Communications. 2023;14(1):436. doi:10.1038/s41467-023-36188-7 PMID: 36702828
  35. Gupta R, Kaczmarzyk J, Kobayashi S, Kurç T, Saltz JH. AI and Pathology: Steering Treatment and Predicting Outcomes. WORLD SCIENTIFIC eBooks. 2023:337-353. doi:10.1142/9789811265679_0019
  36. Gainaru A, Ganyushin D, Xie B, Kurç T, Saltz JH, Oral S, et al. Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics. Communications in computer and information science. 2022:119-138. doi:10.1007/978-3-030-96498-6_7
  37. Fassler D, Torre-Healy LA, Gupta R, Hamilton AM, Kobayashi S, Van Alsten SC, et al. Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression. Cancers. 2022;14(9):2148. doi:10.3390/cancers14092148 PMID: 35565277
  38. Agraz JL, Grenko CM, Chen AA, Viaene AN, Nasrallah MD, Pati S, et al. Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough? IEEE Open Journal of Engineering in Medicine and Biology. 2022;3:218-226. doi:10.1109/ojemb.2023.3234443 PMID: 36860498
  39. Maier‐Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, et al. Metrics reloaded: Recommendations for image analysis validation. arXiv (Cornell University). 2022. doi:10.48550/arxiv.2206.01653
  40. Gupta S, Hu X, Kaan JH, Jin MX, Mpoy M, Chung K, et al. Learning Topological Interactions for Multi-Class Medical Image Segmentation. Lecture notes in computer science. 2022:701-718. doi:10.1007/978-3-031-19818-2_40
  41. Pati S, Baid U, Edwards B, Sheller M, Wang S, Reina GA, et al. Federated learning enables big data for rare cancer boundary detection. Nature Communications. 2022;13(1):7346. doi:10.1038/s41467-022-33407-5 PMID: 36470898
  42. Baid U, Pati S, Kurç T, Gupta R, Bremer E, Abousamra S, et al. Federated Learning for the Classification of Tumor Infiltrating Lymphocytes. arXiv (Cornell University). 2022. doi:10.48550/arxiv.2203.16622
  43. Kaczmarzyk J, Kurç T, Abousamra S, Gupta R, Saltz JH, Koo PK. Evaluating histopathology transfer learning with ChampKit. arXiv (Cornell University). 2022. doi:10.48550/arxiv.2206.06862
  44. Barreiros W, de Melo ACMA, Kong J, Ferreira R, Kurç T, Saltz JH, et al. Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning. Journal of Parallel and Distributed Computing. 2022;164:40-54. doi:10.1016/j.jpdc.2022.02.004
  45. Meirelles ALS, Kurç T, Saltz JH, Teodoro G. Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes. Computer Methods and Programs in Biomedicine. 2022;220:106828. doi:10.1016/j.cmpb.2022.106828 PMID: 35500506
  46. Liu H, Kurç T. Deep learning for survival analysis in breast cancer with whole slide image data. Bioinformatics. 2022;38(14):3629-3637. doi:10.1093/bioinformatics/btac381 PMID: 35674341
  47. Abousamra S, Gupta R, Hou L, Batiste RC, Zhao T, Shankar A, et al. Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. Frontiers in Oncology. 2022;11:806603. doi:10.3389/fonc.2021.806603 PMID: 35251953
  48. Kaczmarzyk J, Abousamra S, Kurç T, Gupta R, Saltz JH. Dataset for tumor infiltrating lymphocyte classification (304,097 image patches from TCGA). Zenodo (CERN European Organization for Nuclear Research). 2022. doi:10.5281/zenodo.6604093
  49. Meirelles ALS, Kurç T, Kong J, Ferreira R, Saltz JH, Teodoro G. Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification. Frontiers in Medicine. 2022;9:894430. doi:10.3389/fmed.2022.894430 PMID: 35712087
  50. Foran DJ, Durbin EB, Chen W, Sadimin ET, Sharma A, Banerjee I, et al. An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities. Journal of pathology informatics. 2022;13:5. doi:10.4103/jpi.jpi_31_21 PMID: 35136672
  51. Hasan M, Kaczmarzyk J, David P, Oblein L, Oentoro J, Abousamra S, et al. A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment. arXiv (Cornell University). 2022. doi:10.48550/arxiv.2204.12283

Publications

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