ECE Associate Professor Anand Sarwate is the recipient of a new NIH award for the project entitled "COINSTAC 2.0: Decentralized, Scalable Analysis of Loosely Coupled Data” through the National Institute on Drug Abuse. This is a 5-year $3 million grant to Georgia State University (Vince Calhoun, PI). The amount subcontracted to Rutgers is $623,113.
In this project, Dr. Sarwate will work with with Dr. Calhoun and researchers at the TReNDS Center to further develop the COINSTAC system for collaborative research, which provides an independent, open, no-strings-attached tool that performs analysis on datasets distributed across different locations. Thus, the step of actually aggregating data is avoided, while the strength of large-scale analyses can be retained. During this new phase they will respond to the need for advanced algorithms such as linear mixed effects models and deep learning, by proposing to develop decentralized models for these approaches and also implement a fully scalable cloud-based framework with enhanced security features. To achieve this, they will incorporate the necessary functionality to scale up analyses via the ability to work with either local or commercial private cloud environments, together with advanced visualization, quality control, and privacy and security features. This suite of new functions will open the floodgates for the use of COINSTAC by the larger neuroscience community to enable new discovery and analysis of unprecedented amounts of brain imaging data located throughout the world. This will also improve usability, training materials, engage the community in contributing to the open source code base, and ultimately facilitate the use of COINSTAC's tools for additional science and discovery in a broad range of applications. Next, they will extend the framework to handle powerful algorithms such as linear mixed effects models and deep learning, and to perform meta-learning for leveraging and updating fit models. And finally, they will test this new functionality through a partnership with the worldwide ENIGMA addiction group, which is currently not able to perform advanced machine learning analyses on data that cannot be centrally located. We will evaluate the impact of 6 main classes of substances of abuse (e.g. methamphetamines, cocaine, cannabis, nicotine, opiates, alcohol and their combinations) using the new developed functionality.
You can find more details on the project from the GSU press release here.
Congratulations, Anand!