firstname.lastname@example.org / (213) 740-7618 / AHF B55C
2010 Ph.D., Computer Science, University of Southern California
2004 M.S., Computer Science, University of Southern California
2000 B.S., Information Management, National Taiwan University
Yao-Yi Chiang, Ph.D., is an Associate Professor (Research) of Spatial Sciences in the Spatial Sciences Institute and Associate Director of the Data Science Institute, University of Southern California, Viterbi School of Engineering.
His general area of research is artificial intelligence and data science, with a focus on information integration and spatial data analytics. He develops computer algorithms and applications that discover, collect, fuse, and analyze data from heterogeneous sources to solve real world problems. Chiang is an expert in digital map processing, pattern recognition, and geospatial information systems (GIS). In addition, he teaches data mining, spatial databases, and mobile GIS.
He has published many articles on automatic techniques for geospatial data extraction and integration. For his paper Querying Historical Maps as a Unified, Structured, and Linked Spatiotemporal Source, he won the first place prize at the Computing Community Consortium (CCC) sponsored Blue Sky Ideas Track Competition at the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2015 (SIGSPATIAL 2015) in Seattle, Washington. The institutions funding his research include the National Science Foundation, Defense Advanced Research Projects Agency, and the National Endowment for the Humanities.
Prior to USC, Chiang worked as a research scientist for Geosemble Technologies (now TerraGo Technologies), which was founded based on a patent of which he was a co-inventor on geospatial-data fusion techniques.
VoPham, T., Hart, J. E., Laden, F., Chiang, Y.-Y. (2018) Emerging Trends in Geospatial Artificial Intelligence (geoAI): Potential Applications for Environmental Epidemiology. Environmental Health, (In press).
Uhl, J. H., Leyk, S., Chiang, Y.-Y., Duan, W., and Knoblock, C. A. (2018) Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections. ISPRS International Journal of Geo-Information, 7(4), 148. doi: 10.3390/ijgi7040148
Wu, J. Wan., Y., Chiang, Y.-Y., Fu, Z. & Deng, M., A Matching Algorithm Based on Voronoi Diagram for Multi-Scale Polygonal Residential Areas. IEEE Access., doi:10.1109/ACCESS.2018.2793302 (preprint), 2018.
Chiang Y.-Y. & Feldman, D., Next Generation Framework for Imagery Recognition and Analysis. The 1st workshop of the NSF project: S:12-S212 Conceptualization: Geospatial Software Institute (GSI), Los Angeles, California, 2017.
Duan, W. & Chiang., Y.-Y., SRC: A Fully Automated Geographic Feature Recognition System. SIGSPATIAL Special 9(3):6-7, 2017, doi:10.1145/3178392.3178396.
Uhl, J.H., Leyk, S., Chiang, Y.-Y., Duan, W. & Knoblock, C.A. (2017) Extracting Human Settlement Footprint from Historical Topographic Map Series Using Context-Based Machine Learning. In Proceedings of the International Land Use Symposium 2017 (Spatial data modelling and visualization to enlighten sustainable policy making), Dresden, Germany, 2017.
Chiang, Y.-Y., Unlocking Textual Content from Historical Maps – Potentials & Applications, Trends, and Outlooks. In K.C. Santosh, M. Hangarge, V. Bevilacqua, & A. Negi (Eds.), Recent Trends in Image Porocessing and Pattern Recognition. Communications in Computer and information Science, 709 (pp. 111-124). Singapore: Springer, 2017.