Daniel Fonner is an adjunct lecturer in the Division of Corporate Communication and Public Affairs, as well as the Associate Director for Research at SMU DataArts, the National Center for Arts Research at SMU. Daniel’s teaching and research focus on data science for social good, employing artificial intelligence to improve public administration and support the arts and culture sector.
Prior to joining SMU, Daniel was a researcher at BOP Consulting in London (UK) and spent time as the Research and Policy Associate at the Greater Pittsburgh Arts Council (PA). He received a Bachelor of Music degree in percussion performance from Duquesne University followed by a Master of Arts Management degree from Carnegie Mellon University, both in Pittsburgh, PA. Daniel then received an M.A. in International Cultural Policy and Management from Warwick University (UK) as a Fulbright Postgraduate Scholar.
Fonner, Daniel and Frank P. Coyle. “Explainable Machine Learning Models for Evaluating Government Grantmaking,” 2022 IEEE International Conference on Big Data (BigData), 2022.