Brian Connolly obtained his PhD in high energy particle physics in 2002. His thesis work, performed at the then highest energy particle collider in the world (the Tevatron), had to do with developing advanced statistical and computational techniques for measuring the mass of a newly discovered sub-atomic particle, the top quark.
After graduating Dr. Connolly embarked on a number of successful data analysis projects at Columbia University and the University of Pennsylvania. His work covered a wide range of topics, from developing sequential analysis techniques for estimating optimal sample data sizes to pioneering the use of graph theory methods for establishing evolutionary trends in merging galaxies.
In recent years, his research has become increasingly multi-disciplinary, as he applied his statistical and computational techniques to fields other than physics and astrophysics: for example, he performed a number of successful investigations in the fields of archaeology, finding trends in the development of lithic technology; and biology, analyzing microarray data in novel ways.
In 2013, as a research fellow at Cincinnati Children’s Hospital, Dr. Connolly took on his biggest data analysis challenge yet: data mining natural language. He is currently developing computational algorithms to understand clinical interpretations of epilepsy and neuropsychiatric notes.