Event Details

The location of things in space and how they change over time is the key to understanding complex environmental phenomena and human-environmental interactions for promoting health and improving quality of life. Today, a significant amount of data contains location and time information, either explicitly, e.g., traffic sensors, air quality sensors, satellite imagery, or implicitly, e.g., images and text documents. However, due to the high data heterogeneity and complexity (e.g., spatial misalignment and non-stationarity), most studies encourage using a single data type or treating the spatial and temporal dimensions as yet another independent variable.

This talk presents spatially enabled machine learning methods that exploit spatial and temporal relationships in multisource, multimodal data to overcome modeling and data challenges, such as limited training data, in handling complex spatiotemporal data. The machine learning methods are demonstrated in two real-world applications: fine-scale prediction of air quality from limited sensors and automatic extraction of structured linked geographic data from historical maps.

This talk concludes by summarizing future research directions on building spatial-enabled computer algorithms for solving real-world problems.

Speaker Bio

Yao-Yi Chiang, Ph.D., is an Associate Professor (Research) in Spatial Sciences, a faculty member in Data Science, the Director of the Spatial Computing Laboratory, the Associate Director of the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California (USC). He is also an Action Editor of GeoInformatica (Springer). In Fall 2021, Dr. Chiang will be joining the Computer Science & Engineering Department at the University of Minnesota as an Associate Professor. Dr. Chiang received his Ph.D. degree in Computer Science from the University of Southern California, his bachelor's degree in Information Management from the National Taiwan University.

His current research area is spatial artificial intelligence. He develops spatially enabled machine learning and data mining methods to discover useful insights from heterogeneous spatial data, including satellite imagery, scanned maps, natural language data, and time-series data. He has received funding from agencies such as NSF, NIH, DARPA, NGA, and NEH and industry partners such as NTT Global Networks, BAE Systems, Conveyancing Liability Solutions, and TerraGo. He was recently a visiting researcher at Google AI (NYC) and a machine learning consultant at the Spatial Computing Group at Facebook.

Before USC, Dr. Chiang worked as a research scientist for Geosemble Technologies and Fetch Technologies in California. Geosemble Technologies was founded on a patent on geospatial data fusion techniques, and he was a co-inventor.