Orhun Aydin, Ph.D.
Assistant Professor
Department of Earth and Atmospheric Sciences
Assistant Professor
Department of Computer Science (by courtesy)
Courses Taught
Machine Learning in GIS and Remote Sensing; Advanced Programming in GIS and Remote Sensing
Education
- Ph.D. in Energy Resources Engineering (Geostatistics), Stanford University
- Ph.D. Minor in Geological Sciences, Stanford University
- M.Sc. in Computer Science, Georgia Institute of Technology
- M.Sc. in Energy Resources Engineering (Geostatistics), Stanford University
- B.Sc. Petroleum Engineering, Middle East Technical University
- B.Sc. Electrical and Electronical Engineering, Middle East Technical University
Research Interests
- Computational Sustainability
- Spatial and Spatiotemporal Artificial Intelligence (GeoAI)
- Urban Sensing
- Integrated Human-Earth System Modeling
- Spatial Game Theory and Prescriptive Learning
- Spatial Optimization for Disaster Response
- Big Geospatial Data Analysis
- Open-Source Geospatial Software
Publications and Media Placements
Peer-Reviewed Journal Articles
- Aydin, O., Osorio-Murillo, C., and Huang, C.C. "Density-based cluster detection at multiple spatial scales via kullback-leibler divergence of reachability profiles." Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. 2022.
- Aydin, O., Osorio-Murillo, C., Butler, K. A., & Wright, D. (2022). Conservation planning implications of modeling seagrass habitats with sparse absence data: a balanced random forest approach. Journal of Coastal Conservation, 26(3), 22.
- Aydin, O., Janikas, M. V., Assunção, R. M., & Lee, T. H. (2021). A quantitative comparison of regionalization methods. International Journal of Geographical Information Science, 35(11), 2287-2315.
- Gu, Y., Aydin, O., & Sosa, J. (2021). Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization. Geosciences, 11(2), 99.
- Wang, Z., & Aydin, O. (2020). Sensitivity analysis for covid-19 epidemiological models within a geographic framework. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19 (pp. 11-14).
- Aydin, O., Janikas, M. V., Assunçao, R., & Lee, T. H. (2018, November). SKATER-CON: Unsupervised regionalization via stochastic tree partitioning within a consensus framework using random spanning trees. In Proceedings of the 2nd ACM SIGSPATIAL international workshop on AI for geographic knowledge discovery (pp. 33-42).
- Aydin, O., & Caers, J. K. (2017). Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework. Tectonophysics, 712, 101-124.
- Shin, Y., Roy, A., Aydin, O., Mukerji, T., & Caers, J. (2016). A Benchmark Synthetic Dataset for Fractured Reservoir. In Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment: Challenges, Processes and Strategies (pp. 555-561). Springer International Publishing.
- Aydin, O., & Caers, J. (2014, October). Exploring structural uncertainty using a flow proxy in the depositional domain. In SPE Annual Technical Conference and Exhibition. OnePetro.
- Aydin, O., & Caers, J. (2013). Image transforms for determining fit-for-purpose complexity of geostatistical models in flow modeling. Computational Geosciences, 17, 417-429.
- Aydin, O. & Walbridge, S. (2022) The Geography of Ocean Plastics. In GIS for Science, ESRI Press
- Urbina, R. P., Aydin, O., & Snow, S. (2021). Management and Analysis of Maritime Geospatial Data During COVID-19: Case Studies, Opportunities and Challenges. In COVID-19 Pandemic, Geospatial Information, and Community Resilience (pp. 123-136). CRC Press.
- Hu, Y., Li, W., Wright, D., Aydin, O., Wilson, D., Maher, O., & Raad, M. (2019). Artificial intelligence approaches. arXiv preprint arXiv:1908.10345.