Claire Monteleoni
Papers
1
Total Citations
31
H-Index
1
About
Claire Monteleoni is a leading researcher in machine learning and climate informatics, pioneering the intersection of artificial intelligence with environmental science. Her work focuses on developing algorithms that adapt to non-stationary environments, particularly for climate change prediction and robotics. Monteleoni's major contributions include advancing online learning theory for shifting data distributions, enabling more accurate long-term climate projections. She co-founded the field of climate informatics, organizing the first workshops that bridged machine learning and climate science communities. Her highly cited work on "Environment selection and hierarchical place recognition" (2015, 31 citations) addresses the critical challenge of sustainable robotic mapping, where computational resources must be managed as robots accumulate vast environmental data over time. This research has implications for autonomous systems operating in dynamic, long-term settings. Monteleoni's impact extends beyond citations; she has received multiple NSF grants and is recognized for mentoring the next generation of interdisciplinary researchers. Her work continues to shape how AI systems learn from and adapt to our changing planet.
Research Focus
Key Achievements
Top Papers
- 1Environment selection and hierarchical place recognition31 citations · 2015