The brain is a tremendously complex system and in order to understand it we are going to need large amounts of data from many different kinds of measurements. Me and my colleagues use data science methods to integrate the information provided by these measurements into a coherent picture. In particular, we develop statistical analysis techniques to decipher the role of networks of brain areas in complex behaviors and in brain disorders, and I implement these techniques in robust, efficient, and openly-available computer software.
University of California, Berkeley (2010)
- April 18, 2022 Grad student McKenzie Hagen received National Science Foundation Graduate Research Fellowship. Hagen is mentored by Ariel Rokem.
- April 18, 2022 McKenzie Hagen received Department of Energy Computational Sciences Graduate Fellowship
- March 10, 2022 Ariel Rokem received a NIH Diversity Supplement to support a post-bacc researcher pursuing a career in biomedical research
- March 7, 2022 Ariel Rokem awarded five-year competing renewal grant from NIH supporting an annual summer institute on neuroimaging and data science at UW
- March 2, 2022 Ariel Rokem receives two NIH subawards for collaborative projects with Stanford University and the University of Texas.
- December 16, 2020 The College Council and Dean have recommended to the Provost that Ariel Rokem be promoted to Research Associate Professor.
- Richie-Halford, A., Cieslak, M., Ai, L. et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 9, 616 (2022). https://doi.org/10.1038/s41597-022-01695-7
- Kruper J, Yeatman JD, Richie-Halford A, et al. Evaluating the reliability of human brain white matter tractometry (in press). Aperture. https://www.biorxiv.org/content/10.1101/2021.02.24.432740v2
- Richie-Halford A, Yeatman JD, Simon N, Rokem A. Multidimensional analysis and detection of informative features in human brain white matter. PLoS Comput Biol. 2021;17(6):e1009136.