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McGovern Institute for Brain Research

Massachusetts Institute of Technology

43 Vassar St, 46-4033F,

Cambridge, MA 02139, USA


My research interests span computer science and neuroimaging, specifically in the areas of applied machine learning, software engineering, and applications of neuroimaging. The primary focus of my 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 dataflow systems for reproducible research. These projects grew naturally from my strong foundation in computational modeling, brain imaging, software engineering, and interest in clinical applications. I am 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 together.



PhD, Cognitive and Neural Systems, Boston University, 2005, Prof. Frank Guenther

B.S. (Honors), Computer Science, National University of Singapore, 1997, Prof. Lonce L. Wyse


Professional Appointments

Principal Research Scientist, McGovern Institute for Brain Research, MIT


Assistant Professor, Department of Otolaryngology, Harvard Medical School

Associate Editor, Frontiers in Brain Imaging Methods, Human Neuroscience, Neuroinformatics



Selected Publications (Full list:

1.     Gorgolewski, K., Burns, C.D., Madison, C., Clark, D., Halchenko, Y.O., Waskom, M.L., Ghosh, S.S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5:13.

2.     Margulies D, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, Bezgin G, Eickhoff S, Castellanos FX, Petrides M, Jefferies E, Smallwood J (2016). Situating the default-mode network in a principal gradient of macroscale cortical organization. Proceedings of National Academy of Sciences.

3.     * Doehrmann, O., * Ghosh, S.S., Polli, F.P., Reynolds, G., Horn, F., Keshavan, A., Whitfield-Gabrieli, S., Hofmann, S.G., Pollack, M., Gabrieli, J.D. (2013) Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry. (* Joint first authors)

4.     Gabrieli, J.D.E., Ghosh, S.S., Whitfield-Gabrieli, S. (2015). Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience. Neuron.

5.     Whitfield-Gabrieli, S., Ghosh, S.S., Nieto-Castanon, A., Saygin, Z., Doehrmann, O., Chai, X.J., Reynolds, G.O., Hofmann, S.G., Pollack, M.H., Gabrieli, J.D. (2016) Brain connectomics predict response to treatment in social anxiety disorder. Molecular Psychiatry.


Technological Innovations

> Nipype: Dataflows for reproducible biomedical research

> VoiceUp: Mobile health voice and data collection

> MURFI: A realtime software for fMRI-based biofeedback

> Audapter: Realtime vocal modification

> MRCancel: MRI communication noise suppression

> Carotid artery diameter estimates from ultrasound

> FlexEffex: Interactive sound effects and music

> Contributions to opensource projects

Selected NIH Research Grants

> R01 - Nipype: Dataflows for Reproducible Biomedical Research (Principal Investigator)

> P41 - ReproNim: A Center for Reproducible Neuroimaging Computation (Director of Data Models and Integration)

> U01 - Connectomes related to anxiety and depression in adolescents. (Informatics lead)

> R01 supplement - Genetic Determinants of Schizophrenia Intermediate Phenotypes (Subaward principal investigator)