Ethan Meyers is a Visiting Associate Professor of Statistics and Data Science at Yale University, an Associate Professor of Statistics at Hampshire College, and a Research Affiliate in the Center for Brains, Minds, and Machines at MIT. He received his undergraduate degree in Computer Science from Oberlin College, his Ph.D. from the Brain and Cognitive Sciences Department at MIT working in Tomaso Poggio’s research group, and was a Postdoctoral Associate at the McGovern Institute for Brain Research at MIT. Ethan Meyers’ research focuses on developing data analysis methods that can extract deeper insights from neural data, and in collaboration with experimental neuroscientists, the goal of his work is to understand the neural algorithms that enable humans and other primates to perform complex behaviors.
What do you do with data science?
Ethan Meyers’ research is at the intersection of Data Science and Neuroscience. In particular, he uses machine learning methods to extract information from high dimensional patterns of neural activity in order to understand how information is transformed as it flows through different brain regions and to understand how information is coded in neural activity. Through collaborations with experimental Neuroscientists, the goal of his work is to understand the neural algorithms that enable complex behaviors. Future directions include developing methods to that can extract insights from larger simultaneously recorded neural data sets, and to create software tools that make it easier for Neuroscientists to conduct reproducible data analyses.
Relevant publications include:
Meyers E (2013). The Neural Decoding Toolbox. Frontiers in Neuroinformatics, 7:8. Meyers E, Qi XL, Constantinidis C (2012). Incorporation of new information into prefrontal cortical activity after learning working memory tasks. Proceedings of the National Academy of Sciences, 109:4651-4656.