Sudipto Ghoshal

About

Sudipto Ghoshal is a researcher at the forefront of spacecraft health monitoring, specializing in the intersection of machine learning and aerospace engineering. His primary research focuses on developing automated, data-driven methods for mode identification and anomaly detection within complex telemetry data from long-duration robotic space missions. Ghoshal’s major contribution lies in pioneering a mixed-method approach that synergizes unsupervised learning algorithms with expert human input. This technique first segments telemetry data temporally and applies clustering to identify nominal operational modes, then flags deviations as potential anomalies. His foundational 2020 paper, "An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data," has garnered 15 citations, establishing a key framework in the field. Earlier work in 2016 further validated this methodology, demonstrating its effectiveness in real-world mission data analysis. By reducing reliance on manual inspection and enabling more automated, scalable health diagnostics, Ghoshal’s research is critical for the safety and longevity of future deep-space exploration, making him a notable figure in intelligent spacecraft operations.

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