Abhishek Dubey
Papers
2
Total Citations
23
H-Index
2
About
Abhishek Dubey is a researcher whose work sits at the intersection of machine learning, anomaly detection, and space systems engineering. He has made notable contributions to the analysis of spacecraft telemetry data, developing innovative methodologies that blend unsupervised learning techniques with human expert knowledge to detect anomalies and operational modes in complex, long-duration robotic space missions. His research addresses one of the most pressing challenges in autonomous space exploration: building reliable, automated systems capable of identifying irregular patterns within vast streams of time series data without requiring constant human supervision. Dubey's 2020 paper, "An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data," has garnered 15 citations, reflecting growing interest in his hybrid approach that leverages the strengths of both computational methods and domain expertise. His earlier 2016 work laid the conceptual foundation for this line of inquiry, demonstrating that large clusters derived from unsupervised learning could effectively characterize nominal spacecraft operations. Together, these contributions represent a meaningful step toward more intelligent, self-monitoring space systems. For students and researchers working in aerospace engineering, machine learning, or fault detection, Dubey's body of work offers practical frameworks and inspiring proof-of-concept applications in one of science's most demanding environments.
Research Focus
Key Achievements
Top Papers
- 1An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data15 citations · 2020
- 2