Senior Data Scientist
Biological Sciences, Health Sciences, Reproducibility & Open Science
Ariel Rokem is a senior data scientist at the University of Washington eScience Institute, and an affiliate of the Institute for Neuroengineering, the Center for Computational Neuroscience, and the Center for Studies in Demography and Ecology at the University of Washington. He received a PhD in neuroscience from UC Berkeley (2010) and additional postdoctoral training in computational neuroimaging at Stanford (2011 – 2015).
He leads a research program in neuroinformatics, the development of data science tools, techniques and methods and their application to the analysis of neural data. One thrust of this research focuses specifically on the application of methods from statistical learning to analysis of diffusion MRI data acquired in human brain. This type of data sheds light on the role that human brain connections play in cognitive abilities, in diverse behaviors, and in neurological and psychiatric disorders.
This research focuses on the interpretation of diffusion MRI at multiple scales, ranging from models of voxel signals (e.g. Rokem et al., 2015, Zheng et al. 2014, Tian et al. 2016) through the level of connections and groups of connections (e.g., Pestilli et al. 2014, Takemura et al. 2015, Yeatman et al. 2014), to the level of individuals and populations (e.g., Huber et al. 2018). Some of the methods and techniques developed in this domain are also applicable to other biomedical imaging technologies (e.g., Mezer et al. 2016, Beyeler et al. 2017, Lee et al. 2017).
Another thrust of the research focuses specifically on the development of systems for analysis (e.g., Mehta et al. 2017, Richford and Rokem, 2018) and sharing (e.g. Yeatman et al. 2018) of large-scale open datasets, to enable research with datasets that are increasingly becoming available through data-sharing initiatives, and to facilitate its reproducibility.
He is a member of the Software and Data Carpentry communities, where he has been an instructor since 2013 and an instructor trainer since 2015, and is currently the chair of the eScience working group in reproducibility and open science. He also directs the annual Summer Institute for Neuroimaging and Data Science (Neurohackademy). A contributor to multiple open-source software projects in the scientific Python ecosystem, he is a member of the editorial board of the Journal for Open Source Software and the Journal for Open Research Software.
- Wednesdays, 10:30 to 11:30 a.m.
- Version control (git/github)
- Reproducible research
- Machine learning/statistical learning
- Neuroscience and biomedical imaging
- Data visualization