Daniel P. Hogan, Ph.D.
Data Scientist

Daniel’s work focuses on data requirements for geospatial machine learning to better understand how much training data is really needed for different performance levels under various conditions. Recently, Daniel has started exploring the use of deep learning for interpreting synthetic aperture radar imagery.

Originally from the Kansas City area, Daniel earned bachelor’s degrees in physics and mathematics from the University of Kansas, coauthoring papers in astroparticle physics, accelerator-based particle physics, and astrobiology.  Subsequently, he earned a Ph.D. from the University of California, Berkeley, where he analyzed data from the LUX dark matter experiment.  Following an Insight Data Science Fellowship in Boston, Daniel joined CosmiQ Works in February 2019.

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