Hamed Khorasgani
Papers
2
Total Citations
23
H-Index
2
About
Hamed Khorasgani is a researcher at the forefront of applying machine learning to spacecraft operations, specializing in anomaly detection and mode identification within complex telemetry data. His work directly addresses the critical challenge of automating the analysis of vast, high-dimensional time-series data generated by long-duration robotic space missions. Khorasgani’s major contribution lies in developing innovative mixed-method frameworks that synergize unsupervised learning algorithms with expert human insight. This approach, detailed in his most-cited work, "An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data" (2020, 15 citations), allows for the efficient segmentation of telemetry into nominal operational modes before pinpointing subtle anomalies. His earlier foundational paper, "An Application of Data Driven Anomaly Identification to Spacecraft Telemetry Data" (2016, 8 citations), established the core methodology of first clustering temporal segments to define baseline behavior. By bridging the gap between raw data and actionable insights, Khorasgani’s research is pivotal for enhancing the safety, reliability, and autonomy of future deep-space exploration, making him a key figure in the evolution of intelligent spacecraft health monitoring.
Research Focus
Key Achievements
Top Papers
- 1An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data15 citations · 2020
- 2