Freshwater ecosystems are facing an escalating water quality degradation globally. During my PhD at Georgia Tech, I focused on leveraging process-based models to study complex water quality/ecology dynamics. With the burgeoning availability of environmental data and computational resources, I have recognized the potential of AI techniques, particularly for large-scale studies. Therefore, as a Schmidt AI in Science Fellow, I aim to develop a completely new AI method, specifically a transformer-based model to identify key drivers of stream nutrient levels. This model will be further used to create high spatiotemporal resolution datasets for broader research areas including large scale water quality assessment and management, process-based model development, and climate change impact studies. My long-term goal is to combine both cutting-edge AI techniques and process-based models for tangible improvements in water quality and ecosystem health.
- Science Mentor: William S. Currie, School for Environment and Sustainability, LSA
- AI Mentor: Samet Oymak, Electrical Engineering and Computer Science, College of Engineering
- Research Theme: Freshwater quality prediction