Tom Vanasse

Tom Vanasse is a machine learning engineer at EnsoData. Tom previously worked as a postdoctoral fellow at the University of Wisconsin-Madison, where they applied linear mixed-effects models, machine learning decoders, and time-evolution analytic models to the fMRI Natural Scenes Dataset to characterize neural mechanisms of memory consolidation. Tom also developed machine learning models to decode conscious states during sleep from fMRI/EEG data prior to awakening. Tom has also worked as a graduate research assistant at UT Health San Antonio, where they applied multivariate methods toward understanding transdiagnostic brain mechanisms. Tom also launched the BrainMap VBM database, a publicly available dataset for meta-analysis of the structural neuroimaging literature. Prior to that, they worked as an undergraduate research assistant at the Center for Investigating Healthy Minds.

Tom Vanasse earned their Ph.D. in Radiological Sciences from The University of Texas Health Science Center at San Antonio. Tom also has a Bachelor's of Science in Physics & Computer Science Certificate from the University of Wisconsin-Madison.

Tom Vanasse reports to Nick Glattard, Co-founder & CTO. Some of their coworkers include Melania Abrahamian - Machine Learning Engineer, Dylan Sadnick - Senior Fullstack Engineer, and Umer Razzaq - Fullstack Engineer.