BWH COMPUTATIONAL PATHOLOGY
Improve human health
Our core mission is to alleviate human suffering by reducing the burden of diseases on individuals and on the population. This mission informs all our activities in developing and applying computational technologies, which we leverage to make an impact on a broad range of human diseases including infectious, cancer, heart, kidney, intestinal, autoimmune, allergies, and neurological disorders.
Advance the field of pathology
Pathology is both a scientific and a medical discipline, involving the study of basic mechanisms of diseases and the diagnosis of diseases using tissue and fluid samples. Our goal is to advance the field of pathology through computational technologies, to improve the understanding, diagnosis, and treatment of human diseases. With this broad view of pathology, we work on a variety of applications such as deep learning/artificial intelligence to improve cancer diagnosis/prognosis from histology images and to create new live bacterial therapeutics to treat infectious or autoimmune diseases.
Develop innovative computational methods
Human diseases often have complex causes and effects on the body. Data needed to analyze human diseases is similarly complex and multi-faceted. These data are also often difficult to acquire, leading to relatively limited dataset sizes. These and other challenges necessitate going beyond application of existing computational methods. Thus, we actively engage in computational research, to develop novel computational models, inference algorithms, integrated pipelines, and hardware. To accomplish this, we leverage a variety of advanced computational disciplines, including Bayesian nonparametric statistics, deep learning and control theory.
Stochastic foundations for single-cell RNA sequencing Single-cell RNA sequencing, which quantifies cell transcriptomes, has seen widespread adoption, accompanied by a proliferation of analytic methods. However, there has been relatively little […]
Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”
This work will leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which […]
Developed by the Computational Pathology Division at Brigham and Women’s Hospital, the Advanced Biomedical Computation (ABC) Seminar, held monthly during the academic year, showcases innovative research from around the globe by up-and-coming […]
Faisal Mahmood, PhD received the 2023 Young Mentor Award from Harvard Medical School for his outstanding contributions to mentorship. The Young Mentor Award is one of several Excellence in Mentoring […]
The ICML Workshop on Computational Biology (WCB) highlights how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular […]