Satrajit S Ghosh

Satrajit S Ghosh 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 center for reproducible neuroimaging computation. His 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 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 dataflow 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 crowd-sourcing.

McGovern Institute for Brain Research at MIT, and the Department of Otolaryngology at Harvard Medical School, United States.

Talk title
Variance is the spice of reproducible research

Talk abstract
Neuroscientific data contain information from an incredible diversity of species, are generated by a plethora of devices, and encapsulate scientific thinking and decision making. The goal of standards development is to model this complex ecosystem in the context of applications and use cases, of current and future technology, and of practicality. Coupled with limited resources and isolated endeavors, such development can be time consuming and often results in limited views of information. This talk will highlight guiding principles for developing standards in neuroscience using reproducible research as a target application. We will demonstrate that delivering reproducible output requires understanding intricate relations between many different components and requires encapsulating this rich information in structured form for reuse. The crux of reproducible research and data standards development is understanding and modeling the sources of variance, and requires collaboration across the community.