Fall 2023 Data Science Seminar Series
Jianwu Wang–AI Based Multi-satellite Earth Remote Sensing and Causal Understanding of Earth Processes

Speaker: Jianwu Wang, Associate Professor, Department of Information Systems and NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP), University of Maryland, Baltimore County (UMBC)

Faculty Host: Irfan Khan, MARR

Abstract: Earth artificial intelligence (AI) has become a research frontier by leveraging AI techniques to understand complex Earth system and help various Earth applications. Challenges for Earth AI include large volume 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. Targeting the quantification of the causal impact of atmospheric processes on the melting of sea ice, the first study proposes a deep learning based causal inference model to infer causation under continuous treatment using recurrent neural networks and a novel probabilistic balancing technique. The second 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.

Biography: Dr. Jianwu Wang is an Associate Professor in Data Science at the Department of Information Systems, University of Maryland, Baltimore County (UMBC). He leads the Big Data Analytics Lab (BDAL) at UMBC and co-leads the NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP). He is also an affiliate faculty at the Department of Computer Science and Electrical Engineering (CSEE), the Joint Center for Earth Systems Technology (JCET), and the Center for Real-time Distributed Sensing and Autonomy (CARDS), UMBC. He received his Ph.D. degree in Computer Science from the Chinese Academy of Sciences in 2007 and did postdoc training at the University of California, San Diego (UCSD). His research interests include Big Data Analytics, Causal AI, Earth Informatics, and Distributed Computing. He has published more than 130 papers. He is/was an associate editor or editorial board member of four international journals and a conference organization committee member of eight conferences. Since joining UMBC in 2015, he has received 20 external grants as PI, Co-PI or Senior Personnel funded by NSF, NASA, DOE, ARL, State of Maryland, and Industry. He received the Early-Career Faculty Excellence award from UMBC in 2019 and the NSF CAREER award in 2020.

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