Organizing Team
The SLC-PFM competition is organized by a diverse team of experts spanning AI, computer vision, pathology, oncology, biomedical engineering, genomics, and epidemiology from leading institutions worldwide.
Memorial Sloan Kettering Cancer Center
Dr. Neeraj Kumar, PhD
Machine Learning Scientist
Department of Pathology, Warren Alpert Center for Digital and Computational Pathology
Dr. Neeraj Kumar, PhD, is a machine learning scientist in MSK’s Department of Pathology and Warren Alpert Center for Digital and Computational Pathology. He specializes in developing computer vision and statistical machine learning techniques for clinically relevant problems in healthcare and medicine. Previously, he served as a Machine Learning Educator at the Alberta Machine Intelligence Institute in Canada and completed postdoctoral training at University of Illinois at Chicago (in Pathology) and University of Alberta (in Computing Science). He has organized the Multi-Organ Nuclei Segmentation Challenge at MICCAI 2018, the Multi-Organ Nuclei Segmentation and Classification Challenge at IEEE ISBI 2020, and the AAAI Symposium on Survival Prediction 2021.
Dr. Chad Vanderbilt, MD
Assistant Professor, Molecular Diagnostics Service
Principal Investigator, Warren Alpert Center for Digital and Computational Pathology
Dr. Chad Vanderbilt, MD, is a molecular pathologist and computational researcher at Memorial Sloan Kettering Cancer Center (MSK), where he serves as Assistant Professor in the Molecular Diagnostics Service and Principal Investigator in the Warren Alpert Center for Digital and Computational Pathology. His work integrates artificial intelligence and computational methods into clinical diagnostics, with particular attention to developing tools that can be practically implemented in patient care. Chad also dedicates significant time to mentoring fellows and junior faculty, helping them gain skills in bioinformatics and AI applications within pathology.
Icahn School of Medicine at Mount Sinai
Dr. Ruchika Verma, PhD
Computational Scientist
Windreich Department of AI and Human Health
Dr. Ruchika Verma, PhD, is a Computational Scientist at the Windreich Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, and the Hasso Plattner Institute of Digital Health. She develops machine learning methods for personalized oncology, focusing on tumor detection, nuclei segmentation, and treatment outcome prediction. Dr. Verma has previously organized the the Multi-Organ Nuclei Segmentation Challenge at MICCAI 2018, the Multi-Organ Nuclei Segmentation and Classification Challenge at IEEE ISBI 2020. She earned her PhD in Biomedical Engineering from Case Western Reserve University, where she received the Outstanding Graduate Career Award. Her expertise includes machine learning, medical image analysis, and computational pathology.
Dr. Gabriele Campanella, PhD
Assistant Professor
Dr. Gabriele Campanella, PhD, is an Assistant Professor at Icahn School of Medicine at Mount Sinai. His research interests are in the field of computational pathology, and focuses on the development of AI-powered clinical-grade decision support systems for aiding clinicians in diagnosing and treating cancer patients. His current work include the development of novel foundation models for pathology slides and the development of computational biomarkers for treatment recommendation.
MD Anderson Cancer Center, University of Texas
Jia Wu, PhD
Associate Professor
Departments of Imaging Physics and Thoracic/Head & Neck Medical Oncology
Jia Wu, PhD, is a tenured Associate Professor in the Departments of Imaging Physics and Thoracic/Head & Neck Medical Oncology (THNMO) at MD Anderson. He is a leader in multimodal machine learning, integrating imaging, pathology, clinical, and molecular data to tackle critical challenges in lung cancer. His innovative research has resulted in high-impact publications and sustained funding from NIH, CPRIT, and Break Through Cancer.
Jianjun Zhang, MD, PhD
Professor
Department of Thoracic Medical Oncology and Department of Genomic Medicine
Jianjun Zhang, MD, PhD, is a tenured Professor in the Department of Thoracic Medical Oncology and the Department of Genomic Medicine at the University of Texas MD Anderson Cancer Center. In addition, He serves as the Director of the Lung Cancer Genomics Program, the Director of the Lung Cancer Interception Program, and the Chair of MD Anderson’s GEMINI Program.
Luisa M Solis Soto, MD
Pathologist
Department of Translational Molecular Pathology
Luisa M Solis Soto, MD, is a pathologist in the Department of Translational Molecular Pathology at MD Anderson. She is Director of the Immunohistochemistry Laboratory and Digital Pathology at TMP. Also, she is co-director of the TMP Immunoprofiling Laboratory (TMP-IL) MD Anderson Moonshot platform.
Rukhmini Bandyopadhyay, PhD
Postdoctoral Fellow
Department of Imaging Physics
Rukhmini Bandyopadhyay, PhD, is a postdoctoral fellow in the Departments of Imaging Physics at MD Anderson. Her research interests include developing deep learning/machine learning-based novel computational framework for cancer diagnosis and prognosis using histopathological images.
Muhammad Waqas, PhD
Postdoctoral Fellow
Department of Imaging Physics
Muhammad Waqas, PhD, is a postdoctoral fellow in the Departments of Imaging Physics at MD Anderson. His primary objective is to improve the theoretical foundations of MIL and provide practical solutions for complex real-world problems for cancer diagnosis and prognosis in computational pathology.
Mayo Clinic
Dr. Hamid Reza Tizhoosh, PhD
Professor
Department of Artificial Intelligence & Informatics
Dr. Hamid Reza Tizhoosh, PhD, is a Professor in the Department of Artificial Intelligence & Informatics at Mayo Clinic. He has extensive expertise in image retrieval systems for digital pathology and developed the Yottixel search engine for large-scale histopathology image archives. His research focuses on content-based image retrieval, self-supervised learning, and AI applications in pathology. He has organized multiple workshops and competitions on AI in medical imaging.
Stony Brook University
Dr. Joel Saltz, MD, PhD
Cherith Chair of Biomedical Informatics
Dr. Joel Saltz, MD, PhD, is the Cherith Chair of Biomedical Informatics at State University of New York at Stony Brook. He is a pioneer in the field of digital pathology and computational analysis of whole slide images. His research spans high-performance computing, machine learning, and biomedical informatics, with a focus on developing methods to extract and analyze features from gigapixel pathology images. He has led numerous large-scale initiatives in computational pathology and biomedical data science.
Jakub Kaczmarzyk
MD-PhD Student (6th year)
Jakub Kaczmarzyk is a sixth-year MD-PhD student at State University of New York at Stony Brook, where he recently completed his PhD in Biomedical Informatics. His research focuses on computational pathology, with an emphasis on explainable AI and clinical prognostics. He develops AI methods to extract clinically meaningful insights from histopathology images, with a strong interest in reproducible science and open-source tools. Jakub aims to bridge the gap between algorithmic innovation and clinical practice, with the long-term goal of translating computational advances into real-world patient care.
University of North Carolina at Chapel Hill
Melissa Troester, PhD
Professor of Epidemiology and Pathology
Melissa Troester, PhD, is Professor of Epidemiology and Pathology at University of North Carolina at Chapel Hill. Her research focuses on integrating, molecular, pathology, and clinical data to optimize breast cancer outcomes.
Katherine Hoadley, PhD
Associate Professor of Genetics
Computational Medicine Program
Katherine Hoadley, PhD, is an Associate Professor of Genetics and a member of Computational Medicine Program at the University of North Carolina at Chapel Hill. Her research focuses on understanding cancer biology, particularly breast cancer, through gene expression analyses and integrative genomic approaches.
Contact the organizing team: slcpfm2025@gmail.com