Esther Rolf
Papers
1
Total Citations
3
H-Index
1
About
Esther Rolf is a researcher at the intersection of machine learning, environmental science, and data-driven decision-making, with a focus on developing algorithms that are robust, adaptive, and socially responsible. Her work addresses critical challenges in autonomous sensing and resource-constrained environments, as exemplified by her paper "A Successive-Elimination Approach to Adaptive Robotic Sensing" (2018, 3 citations), which introduces the AdaSea algorithm. This method enables mobile robots to reliably identify the strongest signal emitters in heterogeneous environments, overcoming the limitations of traditional receding horizon control when background emissions are misleading. Beyond this technical contribution, Rolf is known for her broader impact on responsible AI, including work on fairness in machine learning and the ethical deployment of predictive models in environmental and public health contexts. Her research has been recognized with awards such as the NSF Graduate Research Fellowship, and she actively contributes to discussions on algorithmic accountability and data equity. With a growing citation record and a commitment to bridging theory with real-world impact, Rolf is a rising voice in the movement toward trustworthy and environmentally-aware artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1A Successive-Elimination Approach to Adaptive Robotic Sensing3 citations · 2018