Andreas Puder
Papers
1
Total Citations
9
H-Index
1
About
Andreas Puder is a leading researcher in the fields of anomaly detection, time series analysis, and the safety and security of cyber-physical systems, with a particular focus on medical devices. His most-cited work, "Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models" (2024), has already garnered 9 citations, reflecting its timely impact. In this paper, Puder pioneers a hybrid framework that fuses classical Kalman filtering with modern machine learning, addressing the critical need for robust safety mechanisms in increasingly connected medical devices. His major contribution lies in bridging the gap between traditional statistical methods and AI, enabling more reliable detection of anomalies in time-series data—a challenge vital for patient safety. Puder’s research is driven by the growing automation and connectivity trends in healthcare, where devices must operate securely under stringent conditions. By demonstrating how hybrid models can outperform standalone approaches, he has provided a practical pathway for enhancing the resilience of medical systems. His work not only advances academic understanding but also offers tangible solutions for industry, positioning him as a key figure in the evolution of safe, intelligent medical technology.
Research Focus
Key Achievements
Top Papers
- 1