• Celepcikay, O., Eick, C. A Generic to Estimate Generalization Error of Geo-Regression Techniques, to be submitted to IEEE Transactions on Data Mining and Knowledge Discovery
• Celepcikay, O., Eick, C., REG^2: A Regional Regression Framework for Geo-Referenced Datasets, 17th ACM SIGSPATIAL International Conference on Advances in GIS, 2009
• Celepcikay, O., Eick, C., Ordonez, C. Discovering Regional Patterns in Geo-referenced Datasets Using PCA, 7th Industrial Conference on Machine Learning and Data Mining, MLDM 2009, Germany.
• A. Bagherjeiran, O.U. Celepcikay, R. Jiamthapthaksin, C. Chen, V. Rinsurongkawong, S. Lee, J. Thomas, and C. F. Eick, Cougar^2: An Open Source Machine Learning-Data Mining Development Framework, OSDM, Thailand, 2009.
• Celepcikay, O., Eick, C. A Regional Pattern Discovery Framework using PCA, International Conference on High Dimensional Data Mining, 2008.
• Choo, J., Jiamthapthaksin, R., Chen, C., Celepcikay, O., Giusti, C., and Eick, C., MOSAIC: A Proximity Graph Approach to Agglomerative Clustering, in Proc. of the 9th Intl. Conf. on Data Warehousing and Knowledge Discovery, Germany, 2007.
• Jiamthapthaksin, R., Choo, J., Chen, C., Celepcikay, O., Eick, C., MOSAIC: Agglomerative Clustering with Gabriel Graphs, a book chapter in Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, IGI Press, USA, ISBN: 1935-2646.
My main research interests lie in the field of knowledge discovery and data mining, with applications to geosciences, environmental sciences, and astronomy. My research focuses on designing and implementing an integrated framework to systematically discover regional knowledge in spatial datasets. We work on developing methods to uncover hidden correlation patterns and developing regional regression tools that capture spatially varying relationships among attributes. We developed a regional regression framework, called REG^2, that discovers regional regression functions that are associated with contiguous areas in the subspace of the spatial attributes which we call regions.
I teach data mining, big data analytics, database management systems, object-oriented analysis and design, web programming, programming languages. One important aspect of my teaching philosophy is that students must ultimately take responsibility for their own learning. I try to make my classes very interactive and encourage students to earn participation points by asking questions or solving in-class exercises. Students can always expect that I am approachable and available to answer questions, and will happily invest in their academic success.
- Outstanding Contribution to Education Award - Public Education Category, HPS, May 2011
- Graduate Fellowship, University of Houston, 2006