Abstract

Ecological monitoring increasingly relies on drones, distributed sensors, and artificial intelligence to observe dynamic environments. Yet most systems collect data on predetermined schedules rather than adapting to changing ecological conditions. This talk presents my research on autonomous sensing systems that translate ecological dynamics into system-level objectives (SLOs) and use edge AI to adapt observations in real time. Drawing on wildlife monitoring in Kenya, I demonstrate how behavior-adaptive drones can respond to animal behavior to improve scientific data quality while minimizing disturbance. I then discuss how these principles extend to distributed, multimodal sensing and my ongoing research on coastal salt marsh monitoring, where drones can use environmental context and anomaly detection to guide targeted surveys of fragile, hard-to-reach ecosystems. Looking forward, I envision AI-driven ecological sensing networks that use predictive models and scientific objectives to determine what, where, and when to observe, making environmental monitoring responsive rather than retrospective.

Speaker Bio

Jenna Kline is a Postdoctoral Associate in the Department of Civil and Environmental Engineering at MIT, where she works in the Nepf Lab on autonomous sensing systems for coastal monitoring. She received her Ph.D. in Computer Science and Engineering from The Ohio State University in 2026. Her research lies at the intersection of computer systems, edge AI, and ecology, developing autonomous drones and distributed sensing systems that adapt to changing environmental conditions. Her work spans wildlife behavior monitoring, multimodal ecological sensing, and coastal resilience, making environmental observations more responsive, scalable, and scientifically useful. She is also a co-lead of the WILDLABS Edge Computing for Conservation group, which connects researchers and practitioners developing biodiversity monitoring technology.

Please join us in SEEC S127 or on Zoom.