Seminars

More Information Coming Soon!

John Kitchin works at the intersection of machine learning, data science, and scientific programming with science and engineering. He develops software for modeling materials, solving engineering problems, and writing scientific documents. He uses these tools to model catalysts with applications in energy, to solve inverse problems in engineering, and to find new approaches in developing surrogate models for engineering systems. 

Kitchin completed his B.S. in chemistry at North Carolina State University. He completed an M.S. in materials science and a Ph.D. in chemical engineering at the University of Delaware in 2004 under the advisement of Dr. Jingguang Chen and Dr. Mark Barteau. 

He received an Alexander von Humboldt postdoctoral fellowship and lived in Berlin, Germany for 1½ years studying alloy segregation with Karsten Reuter and Matthias Scheffler in the Theory Department at the Fritz Haber Institut. Kitchin began a tenure-track faculty position in the Chemical Engineering Department at Carnegie Mellon in January 2006. He was awarded a DOE Early Career award in 2010. He received a Presidential Early Career Award for Scientists and Engineers in 2011. He completed a sabbatical in the Accelerated Science group at Google learning to apply machine learning to scientific and engineering problems in 2018. In 2023, he was the recipient of the AIChE Award for Innovation in Chemical Engineering Education.