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About

Fabiana Di Ciaccio is a researcher at the forefront of underwater robotics and maritime navigation, specializing in sensor fusion and deep learning for orientation estimation. Her work addresses a critical challenge in autonomous underwater vehicles (AUVs): achieving accurate attitude estimation—specifically roll, pitch, and yaw—in the presence of complex, irregular noise from sensors and the underwater environment. Di Ciaccio’s major contributions include the development of the DANAE (Denoising Autoencoder for Underwater Attitude Estimation) family of models, which leverage deep learning to intelligently filter noisy sensor data, significantly improving the reliability of Attitude and Heading Reference Systems (AHRS) for underwater robots. Her most cited paper, "DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation" (2021), has garnered 10 citations, while related works on deep model optimization for embedded devices and roll/pitch estimation at sea have collectively received over 30 citations. By demonstrating that deep neural networks can be effectively deployed on low-cost, embedded hardware, Di Ciaccio is making precise underwater navigation more accessible for environmental monitoring and marine robotics. Her innovative fusion of deep learning with classical navigation systems positions her as a key contributor to the next generation of intelligent, autonomous maritime platforms.