Papers
1
Total Citations
43
H-Index
1
About
Mike Risi is a robotics researcher at the forefront of marine exploration, specializing in the intersection of computer vision, machine learning, and autonomous underwater vehicles. His work addresses a fundamental challenge in oceanography: scaling up observations in the vast, three-dimensional deep sea. Risi’s key contribution is the development of novel algorithms that enable robotic underwater vehicles to visually track and follow deepwater animals in real time. By integrating machine learning with robotic control, his systems allow for unprecedented, non-invasive behavioral studies of elusive marine life. His most-cited paper, "Visual tracking of deepwater animals using machine learning-controlled robotic underwater vehicles" (2021, 43 citations), demonstrates how these autonomous platforms can locate and track animals of interest, effectively turning robots into mobile field biologists. This work is pivotal for advancing our understanding of deep-sea ecosystems, which remain poorly explored yet are critical to global ecological function. Risi’s research is not only technically innovative but also directly addresses the urgent need for scalable observation tools in ocean science, making him a key figure in the emerging field of robotic marine biology.
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Top Papers
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