I have used, created, and developed Deep Learning, Machine Learning, & numerical methods to pre-process data, create workflows, identify key markers, and create predictive models, such that the results could be interpreted by another discipline (psychology, neuroscience, neuroergonomics). I am an adaptable organized problem-solver who aims to accomplish the necessary work to succeed, striving to learn and execute data analysis methods in a stimulating and diverse working environment.
Jamilah received a B.S. degree in electrical engineering from the State University of Binghamton, New York in 2003 and PhD. degree in mechanical engineering via the NSF IGERT Program in Computational Science and Engineering from University of California, Santa Barbara with emphasis in control theory, dynamical systems, and numerical analysis in 2012; with application to neuroscience and psychophysical experimentation. She is currently a Data Scientist, a member of IEEE since 2016, a member of Sigma Xi, and a member of the Frontiers Neuroergonomics Editorial Board.