After receiving his PhD in physics at Yale University, Jonathan worked as a researcher studying phase transitions in the very early universe. His academic work focused on developing theoretical and computational tools to understand the statistical properties of very hot, strongly interacting matter. After leaving academia, Jonathan worked in scientific publishing as an assistant editor for the Physical Review, a leading physics journal.
Prior to joining PlaceIQ, Jonathan spent several years in the quantitative trading industry where he applied large-scale optimization and machine learning techniques to tick-level equity pricing data and developed several real-time trading algorithms and analytics platforms. Jonathan brings a wide-rage of computational and mathematical skills to PlaceIQ as well as a passion for machine learning, visualization and building large data analysis pipelines.