Ghosh, SatrajitPrincipal Research Scientist

    Satrajit Ghosh

    Principal Research Scientist

    43 Vassar Street, Office: 46-4033 F
    Cambridge, Massachusetts 02139
    Phone: (617) 324-3544
    Connect: WebsiteLinkedIn / ORCID / Google Scholar / PubMed / Harvard Profile / GitHub


    Ph.D., Cognitive and Neural Systems, Boston University, 2005
    B.S., Computer Science, National University of Singapore, 1997


    Satrajit Ghosh received his BS (Honors) degree in Computer Science at the National University of Singapore and his PhD in Cognitive and Neural Systems from Boston University. He is a Principal Research Scientist at the McGovern Institute for Brain Research at MIT and an Assistant Professor in the Department of Otolaryngology at Harvard Medical School. He is also the Director of Data Models and Integration project of ReproNim, an NIH P41 Center for Reproducible Neuroimaging Computation.

    His research interests span computer science and neuroimaging, specifically in the areas of applied machine learning, signal processing, and translational medicine. His current research portfolio brings together speech, brain imaging, and informatics to address gaps in scientific knowledge from three perspectives:

    1. Speech and clinical applications: Understanding speech mechanisms and applying speech technology to healthcare, specifically psychiatric, neurodegenerative, and speech motor control disorders.

    2. Machine learning for personalized medicine: Integrating imaging, genetic, and behavioral data using machine learning algorithms for clinical diagnosis and treatment prediction.

    3. Neuroinformatics and reproducible research: Establishing standards for organizing scientific information and creating performant tools to address the data deluge and reproducibility.

    The primary focus of his research group is to develop knowledge discovery platforms by integrating a set of multidisciplinary projects that span precision medicine in mental health, imaging genetics, machine learning, and data flow systems for reproducible research. He is a lead architect of the Nipype dataflow platform, an ardent proponent of decentralized and distributed Web solutions for data sharing, querying, and computing, and a strong believer in solving problems through collaboration and crowdsourcing.

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