Rate My Professor Elvan Ceyhan

Ceyhan's research focuses on statistical machine learning, random geometric graphs, network optimization, proximity catch digraphs, spatial clustering and segregation testing, and applications in medical data analysis and ecological modeling. He has published over 58 peer-reviewed articles in journals such as Machine Learning, Discrete & Computational Geometry, ESAIM: Probability and Statistics, and Journal of Machine Learning Research. Notable publications include "Classification using proximity catch digraphs" (Machine Learning, 2020), "On the number of weakly connected subdigraphs in random kNN digraphs" (Discrete & Computational Geometry, 2021), "Law of large numbers for a two-dimensional class cover problem" (ESAIM: Probability and Statistics, 2021), and "Classification of imbalanced data with a geometric digraph family" (Journal of Machine Learning Research, 2016). His scholarship has been cited over 1,185 times. Ceyhan has received numerous awards, including the Marie Kraska Award for Excellence in Teaching (2024) from Auburn University, the Phi Kappa Phi Love of Learning Award (2024), the TWAS Young Affiliate Fellowship (2012-2016), and election to the International Statistical Institute. He serves as President of the Alabama-Mississippi Chapter of the American Statistical Association, Co-Director of Auburn's Center for Data Science Innovation, and has been an associate editor for Computational Statistics & Data Analysis (2008-2016) and Journal of Probability and Statistical Sciences (2007-2013, 2021-present). He has delivered over 90 invited talks worldwide and secured funding from NSF, ONR, Simons Foundation, and others as principal investigator.