satra.github.com/index.html at master · satra/satra.github.com · GitHub

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> [Group page](https://sensein.group/) | [CV](assets/CV_Latest.pdf)

> Email: satra at mit dot edu

> [Github](https://github.com/satra) | [Twitter](https://twitter.com/satra_)

> Office:  46-4033F, 43 Vassar St, Cambridge, MA 02139

> Tel:  +1.617.324.3544 | [Map](http://whereis.mit.edu/?go=46)

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#### Who am I

I am the Director of Open Data in Neuroscience Initiative and a Senior Research Scientist
at the [McGovern Institute for Brain Research](http://mcgovern.mit.edu/) at MIT, an
Assistant Professor of Otolaryngology - Head and Neck Surgery at Harvard Medical School,
and a faculty member in the [Speech and Hearing Biosciences and
Technology](http://dms.hms.harvard.edu/shbt/) program in the Harvard Division of
Medical Sciences. I am a computer scientist and computational neuroscientist by training
and direct the [Senseable Intelligence Group](https://sensein.group/).

#### What do I do

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

1) **Speech and clinical applications:** Understanding the neural basis and
translational applications of human spoken communication and applying speech
technology to healthcare, specifically psychiatric, neurodegenerative, and
speech motor control disorders. More generally, to see the spoken signal as
a window into our mind and health, more broadly.

2) **Machine learning for personalized medicine:** Integrating imaging, genetic,
and behavioral data using machine learning algorithms for clinical diagnosis,
prognosis, and treatment prediction. We link predictability with explainability
and interpretability, alongside ethical and diverse data and models.

3) **Neuroinformatics and reproducible research:** Preserving information for
reproducible research and knowledge generation. We do this by establishing
standards for organizing scientific information and creating performant tools
to address the data and information deluge and reproducibility.

[My research group](https://sensein.group/) develops robust, predictive models
around human health and novel analytic platforms, and establishes
collaborations across disciplines. I was one of the lead architect of the
[Nipype dataflow platform](http://nipy.org/nipype), an ardent proponent of
decentralized and distributed Web solutions for data sharing, querying, and
computing, and a strong believer in solving problems together.

Visit our [group page](https://sensein.group/) for more details.

#### Selected Publications ([Full list here](pubs.html))

* Low DM, Rao V, Randolph G, Song PC, Ghosh SS. (2024) Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings. PLOS Digital Health.

* Park J, Wang J, Guan W, Gjesteby LA, Pollack D, Kamentsky L, Evans NB, Stirman J, Gu X, Zhao C, Marx S, Kim ME, Choi SW, Snyder M, Chavez D, Su-Arcaro C, Tian Y, Park CS, Zhang Q, Yun DH, Moukheiber M, Feng G, Yang XW, Keene CD, Hof PR, Ghosh SS, Frosch MP, Brattain LJ, Chung K. Integrated platform for multiscale molecular imaging and phenotyping of the human brain. Science. 2024. doi:10.1126/science.adh9979. Epub 2024 Jun 14. PubMed PMID: 38870291.

* Low DM, Bentley KH, Ghosh SS (2020). Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investig Otolaryngol.

* Sitek KR, Gulban OF, Calabrese E, Johnson GA, Lage-Castellanos A, Moerel M, Ghosh SS, De Martino F (2019). Mapping the human subcortical auditory system using histology, post mortem MRI and in vivo MRI at 7T.  Elife.

* Guell X, Schmahmann JD, Gabrieli J, Ghosh SS (2018). Functional gradients of the cerebellum. Elife

* McClure P, Zheng C, Kaczmarzyk J, Rogers-Lee J, Ghosh S, Nielson D, Bandettini P, Pereira F (2018) Distributed Weight Consolidation: A Brain Segmentation Case Study In: Neural Information Processing Systems

* Doehrmann O, Ghosh SS, Polli FE, Reynolds GO, Horn F, Keshavan A, Triantafyllou C, Saygin ZM, Whitfield-Gabrieli S, Hofmann SG, Pollack M, Gabrieli JD (2013). Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry.

* Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform.

* Ghosh SS, Matthies ML, Maas E, Hanson A, Tiede M, Ménard L, Guenther FH, Lane H, Perkell JS (2010). An investigation of the relation between sibilant production and somatosensory and auditory acuity. J Acoust Soc Am.

| Selected NIH Research Grants                                                                                 |
| ------------------------------------------------------------------------------------------------------------ |
| U24 - BBQS AI Resource and Data Coordinating Center (BARD.CC) (Principal Investigator) |
| R24 - DANDI: Distributed Archives for Neurophysiology Data Integration (Principal Investigator) |
| P41 - ReproNim: A Center for Reproducible Neuroimaging Computation (Director of Data Models and Integration) |
| U24 - An extensible brain knowledge base and toolset spanning modalities for multi-species data-driven cell types (Principal Investigator) |
| Bridg2AI - Vocal biomarkers of health (Principal Investigator) |

| Technological Innovations                              |
| ------------------------------------------------------ |
| [DANDI](https://dandiarchive.org): A data repository and collaboration ecosystem for neurophysiology |
| [Reproschema](https://repronim.org/reproschema): Mobile voice and survey data collection |
| [MumbleMelody](https://sensein.group/mumble_melody_project/): Realtime vocal modification |
| [Senselab](https://sensein.group/senselab/): A biometrics toolkit |
| [Nobrainer](https://github.com/neuronets/nobrainer): A deep learning toolkit for neuroimaging |
| [Nipype](http://nipy.org/nipype): Dataflows for reproducible biomedical research |
| [Pydra](https://github.com/nipype/pydra): A new workflow engine |
| [MURFI2](https://github.com/gablab/murfi2/): A realtime software for fMRI-based biofeedback |

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