Ilaria Bloise
Papers
1
Total Citations
22
H-Index
1
About
Ilaria Bloise is a researcher at the forefront of deep learning and optimal control for aerospace guidance systems. Her work centers on developing intelligent, data-driven architectures for autonomous spacecraft operations, with a particular emphasis on planetary landing and entry, descent, and landing (EDL) technologies. Bloise’s most impactful contribution is her pioneering 2020 paper, “A recurrent deep architecture for quasi-optimal feedback guidance in planetary landing,” which has garnered 22 citations. In this work, she introduced a novel recurrent neural network framework that approximates optimal feedback control policies in real time—a critical advancement for precision landing on large planetary bodies like the Moon and Mars. By enabling quasi-optimal guidance without the computational burden of traditional optimization solvers, her approach directly supports future human and robotic exploration missions. Bloise’s research bridges the gap between deep learning and aerospace engineering, offering scalable, robust solutions for autonomous navigation in uncertain environments. Her achievements have positioned her as a rising expert in intelligent guidance systems, with implications for both planetary science and the broader field of autonomous aerospace control.
Research Focus
Key Achievements
Top Papers
- 1