Talk: AI for causal understanding of Earth processes, 5/10

Earth artificial intelligence (AI) has become a research frontier by leveraging AI techniques to understand the complex Earth system and help various Earth applications. Challenges for Earth AI include a large volume of available data, spatial-temporal high-dimensionality, incompatible data from multiple sources, data-driven causal understanding of the Earth system. This talk will present two related Earth AI studies. The first study proposes a Time-Series Causal Neural Network (TS-CausalNN) - a deep learning technique to discover contemporaneous and lagged causal relations simultaneously from non-stationary and non-linear Earth observation time series data. The second one studies how to leverage deep domain adaptation techniques and multiple satellite data to improve cloud remote sensing retrieval. Both studies use real-world Earth data to evaluate their advantages over state-of-art approaches.

Dr. Jianwu Wang is an Associate Professor in UMBC's Department of Information Systems. He leads the Big Data Analytics Lab (BDAL) and co-leads the NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP). He is also an affiliate faculty in CSEE and the Joint Center for Earth Systems Technology (JCET).