Somnath Deb

About

Somnath Deb is a researcher at the forefront of spacecraft health monitoring and anomaly detection, with a primary focus on developing automated, data-driven methods for analyzing complex telemetry data from long-duration robotic space missions. His major contributions lie in pioneering mixed-method approaches that synergize unsupervised machine learning with human expert input to identify subtle anomalies in time-series data. In his most-cited work, "An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data" (2020, 15 citations), Deb outlines a framework that first segments telemetry into temporal clusters representing nominal operations, then flags deviations for expert review—a technique that enhances both detection accuracy and operational efficiency. His earlier foundational paper, "An Application of Data Driven Anomaly Identification to Spacecraft Telemetry Data" (2016, 8 citations), demonstrates the practical utility of this method in real mission scenarios, establishing a scalable paradigm for autonomous spacecraft health management. Deb’s work is notable for bridging the gap between raw sensor data and actionable insights, reducing reliance on manual monitoring while maintaining human oversight. His research has significant implications for future deep-space exploration, where communication delays demand robust, onboard anomaly detection.

Research Focus

Key Achievements

Top Papers

1. 1
   An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data

   15 citations · 2020
2. 2
   An Application of Data Driven Anomaly Identification to Spacecraft Telemetry Data

   8 citations · 2016

Key Collaborators

Contact & Links