A group of three University of Michigan faculty members will lead the Advanced Computational Neuroscience Network project as a “spoke” in the Midwest Big Data Hub program funded by the National Science Foundation.
The Principal Investigator is Richard Gonzalez, Amos N. Tversky Collegiate Professor of the U-M Psychology Department, who has joint appointments in Statistics and Marketing, is Director of the Research Center for Group Dynamics, Research Professor in the Center for Human Growth and Development, and has affiliations with the U-M Comprehensive Cancer Center and the Center for Computational Medicine and Bioinformatics.
Co-PI’s are George Alter, professor in the History Department and the Institute for Social Research, and Ivo Dinov, associate professor in the School of Nursing and School of Medicine and Director of Statistics Online Computational Resources (SOCR), and associate director for Education and Training of the Michigan Institute for Data Science (MIDAS).
All three are affiliated faculty of MIDAS.
The ACNN program will leverage rapid technological development in sensing, imaging, and data analysis to facilitate new discoveries in neuroscience, and will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral sciences together with partnerships in academia, industry and government. ACNN will address three specific problems relating to Big Data in neuroscience:
- data capture, organization and management involving multiple centers and research groups
- quality assurance, preprocessing and analysis that incorporates contextual metadata
- data communication to software and hardware computational resources that can scale with the volume, velocity and variety of neuroscience data sets.
ACNN is a collaboration between U-M, Ohio State University, Indiana University, and Case Western Reserve University.
The BD Hubs and Spokes programs are part of a larger effort at NSF to advance data science and engineering. In Fiscal Year 2017, NSF will invest more than $110 million in Big Data research.