Sustainable development is a crucial concern in the modern world, as environmental challenges threaten the planet's ecological balance. In this context, machine learning has emerged as a potent tool for analyzing vast amounts of data to understand complex systems, making it crucial for addressing sustainability issues. However, data-driven models heavily rely on the quantity and quality of available labeled data. They also struggle to generalize beyond their training datasets. Additionally, these AI models often lack explainability, limiting their scientific usage as they may not always adhere to known laws of physics, such as the conservation of mass or energy. In contrast, physical models are grounded in scientific principles and can easily explain the relationship between input and output variables. However, they may struggle to extract information directly from data, and their simplicity might overlook important parameters. Furthermore, physical models typically have coarse resolution and are confined to specific time intervals and regions. By leveraging the wealth of environmental data alongside machine learning algorithms and knowledge-based models, this talk will explore how hybrid models can support sustainable environmental monitoring, including efficient disaster management, polar ice monitoring, and sea-level-rise uncertainty reduction.

Dr. Maryam Rahnemoonfar is an Associate Professor of Computer Science and Engineering with a joint appointment in the Department of Civil and Environmental Engineering at Lehigh University's P.C. Rossin College of Engineering and Applied Science. She is the Director of the Computer Vision and Remote Sensing Laboratory (Bina lab). Her research interests include Data Science for Sustainability, Deep Learning, Computer Vision, AI for Social Good, Remote Sensing, and Document Image Analysis. Her research specifically focuses on developing novel machine learning and computer vision algorithms for heterogeneous sensors such as Radar, Sonar, Multi-spectral, and Optical. Her research projects have been funded by several awards as a Lead PI, including NSF HDR institute Award, NSF BIGDATA award, Amazon Academic Research Award, Amazon Machine Learning award, Microsoft, and IBM. She is passionate about discovering actionable insights in data and leading interdisciplinary research teams and projects to solve environmental and humanitarian problems.

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