Faisal Mahmood, PhD
Harvard Medical School
Brigham and Women’s Hospital
The Broad Institute of MIT and Harvard
Dana-Farber/Harvard Cancer Center
PhD: Okinawa Institute of Science and Technology
ABOUT THE LAB
Mahmood Lab aims to utilize machine learning, data fusion, and medical image analysis to develop streamlined workflows for objective diagnosis, prognosis, and biomarker discovery. We are interested in developing automated and objective mechanisms for reducing interobserver and intraobserver variability in cancer diagnosis using artificial intelligence as an assistive tool for pathologists. The lab also focuses on the development of new algorithms and methods to identify clinically relevant morphologic phenotypes and biomarkers associated with response to specific therapeutic agents. We develop multimodal fusion algorithms for combining information from multiple imaging modalities, familial and patient histories and multi-omics data to make more precise diagnostic, prognostic and therapeutic determinations. We are affiliated with the Harvard Data Science Initiative; the Harvard Bioinformatics and Integrative Genomics (BIG) program; the Cancer Data Science Program at the Dana-Farber Cancer Institute and the Cancer Program at the Broad Institute of Harvard and MIT.
To learn more about our work visit the the official lab website https://faisal.ai/
Dr. Mahmood is an Associate Professor of Pathology at Harvard Medical School and the Division of Computational Pathology at the Brigham and Women’s Hospital. He received his Ph.D. in Biomedical Imaging from the Okinawa Institute of Science and Technology, Japan and was a postdoctoral fellow at the department of biomedical engineering at Johns Hopkins University. His research interests include pathology image analysis, morphological feature, and biomarker discovery using data fusion and multimodal analysis.
LATEST LAB NEWS AND OPENINGS
Mahmood Lab’s Pan-cancer integrative histology-genomic analysis is featured on cover of Cancer Cell
Mahmood Lab’s deep learning-enabled assessment of cardiac transplant rejection study is published in Nature Medicine
Mahmood Lab’s study on AI-based cancer origin prediction using conventional histology is published in Nature
Mahmood Lab’s CLAM method, A Deep-Learning-based Pipeline for Data Efficient and Weakly Supervised Whole-Slide-level Analysis, published in Nature Biomedical Engineering