Jenna Marie Kline
Ohio State University
kline.377@osu.edu
Bio
Jenna M. Kline is a PhD candidate in Computer Science and Engineering at The Ohio State University, where she develops autonomous, adaptive vision-based remote sensing systems for dynamic environments. Her research focuses on agentic, multi-modal systems that leverage edge AI, computer vision, and robotics for real-time environmental monitoring and wildlife conservation. Jenna has published extensively in high-impact venues, including Methods in Ecology and Evolution and conferences such as ACM/IEEE SEC and IEEE ACSOS. Her work spans autonomous UAV systems, animal behavior monitoring, and edge computing frameworks for adaptive field data collection. Her research has been recognized with multiple awards, including the Graduate Student Research Award from OSU’s Computer Science Department, Best Engineering Oral Presentation at the Edward F. Hayes Advanced Research Forum, and Best Poster awards at IEEE ACSOS.
Areas of Research
- Robotics
Edge AI-Enabled Autonomous Environmental Sensing Systems
Recent advances in edge computing and autonomous systems enable unprecedented opportunities for intelligent environmental monitoring. My research develops agentic, multi-modal remote sensing systems that leverage these technologies for real-time adaptive sampling. I create intelligent agents that dynamically adjust sensing strategies based on environmental conditions, integrating computer vision, robotics, and edge AI for autonomous field decisions.
Traditional environmental monitoring relies on static deployments that cannot adapt to changing conditions. My systems process multi-modal sensor data locally, recognize environmental events, and autonomously reconfigure sampling behavior. They detect ecological disturbance indicators and automatically increase sampling resolution, or identify optimal biodiversity assessment locations through real-time habitat analysis.
This work bridges cutting-edge AI with practical environmental needs through interdisciplinary collaboration with ecologists and conservation biologists. By embedding intelligence into sensing platforms, my research enables efficient resource utilization while capturing previously undetectable environmental dynamics.
My future vision involves coordinated multi-agent systems providing unprecedented insights into ecosystem health, climate change impacts, and conservation effectiveness. This represents a transformative step toward intelligent environmental monitoring that adapts and learns alongside natural systems, moving beyond static data collection to dynamic, responsive ecological sensing.