Andrew Nager
Papers
1
Total Citations
2
H-Index
1
About
Andrew Nager is a rising researcher at the intersection of underwater acoustics and machine learning, whose work is shaping the future of autonomous underwater vehicle (AUV) operations. His primary focus lies in modeling and predicting acoustic transmission loss in complex, range-dependent undersea environments—a critical challenge for reliable underwater communication and navigation. In his most-cited paper, "Machine learning transmission loss simulations in complex undersea environments with range-dependent bathymetry" (2023, 2 citations), Nager demonstrates how bathymetric variability introduces scattering and multipath effects that degrade acoustic signals. By leveraging machine learning, he moves toward building a "sound-aware" framework that equips AUVs with real-time knowledge of their acoustic environment, enabling more robust mission planning and communication. Though early in his career, Nager’s work bridges the gap between physical oceanography and artificial intelligence, offering a scalable alternative to computationally expensive traditional simulations. His contributions are particularly notable for their practical implications: improving the autonomy and reliability of underwater vehicles in unpredictable, real-world seabed conditions. As the field of marine robotics grows, Nager’s research promises to be foundational for next-generation, environment-aware underwater systems.
Research Focus
Key Achievements
Top Papers
- 1