“Creating novel computational models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applying these findings to develop new diagnostic tests and therapeutic interventions to improve patient care.“
email: ggerber#bwh.harvard.edu, physical address: Hale Building 8002M, 60 Fenwood Road, Boston, MA 02115
Positions
- Graduate StudentsI am always excited to work with talented graduate students with interests relevant to my lab, which focuses on developing novel machine learning/computational biology/wet lab approaches to further understanding of the microbiome–the trillions of microbes living on and within us. This fascinating, complex and dynamic ecosystem is crucial for human health, and when disrupted may […]
- Postdoctoral Fellow, Deep Learning for MicrobiomePost-doctoral positions available (with flexible start dates) to develop novel deep learning approaches to further understanding of the microbiome–the trillions of microbes living on and within us. This fascinating, complex and dynamic ecosystem is crucial for human health, and when disrupted may contribute to a variety of diseases including infections, arthritis, allergies, cancer, heart and […]
- Research Scientist, Machine Learning for MicrobiomeThe Microbiome AI/Deep Learning Lab in the Massachusetts Host-Microbiome Center and Division of Computational Pathology at Brigham and Women’s Hospital/Harvard Medical School is seeking a scientist with experience in machine learning. You will develop, deploy, and apply machine learning approaches, with a special emphasis on deep learning, to a variety of microbiology data sources. Applications will include forecasting microbial population dynamics […]
Lab News
- “MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics” is mSystems Editor’s Pick
Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software […] - Gerber lab study showing gut metabolites predict C. diff recurrence
Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Using broad metabolomics data and statistics and machine learning models, Jen Dawkins, a HST PhD […] - The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome
Through a $3.3M grant from the Massachusetts Life Science Center and in-kind support from Brigham and Women’s Hospital and Mass General Brigham, the BWH Massachusetts Host-Microbiome Center (MHMC) and Division of Computational Pathology will establish a new lab to develop and apply advanced AI/deep learning technologies to microbiome research. Dr. Georg Gerber, Chief of BWH […] - $2.9M grant from the National Science Foundation “The rules of microbiota colonization of the mammalian gut”
The Gerber lab in collaboration with the Wang lab at Columbia and the Gibson Lab at BWH and have received a $2.9M grant from the National Science Foundation to develop and apply novel computational and experimental methods to elucidate fundamental rules governing the formation and maintenance of complex microbial ecosystems in the mammalian gut. Abstract: Microbiomes, or […]