老司机福利网

Skip to main content

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., Assun莽茫o, 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., Assun莽ao, 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.
Peer-Reviewed Book Chapters
  • 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.
Media Placement