Papers
1
Total Citations
66
H-Index
1
About
Yi Su is a leading researcher in time series analysis and anomaly detection, with a focus on developing robust, unsupervised methods for real-time monitoring. Their most cited work, "Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality" (2020, 66 citations), addresses a critical challenge in event-sensitive applications—from robotic system monitoring to smart sensor networks and data center security. Su’s key contribution lies in creating a detection scheme that operates without labeled data while handling complex, multi-seasonal patterns, a common hurdle in diverse, real-world data streams. This work has been foundational for researchers and engineers seeking scalable solutions for automated anomaly detection in dynamic environments. Beyond this, Su’s research explores the intersection of algorithmic efficiency and practical deployment, making their work highly cited and influential in the fields of cybersecurity, IoT, and industrial automation. Their achievements underscore a commitment to advancing unsupervised learning techniques that bridge the gap between theoretical rigor and operational reliability, offering impactful tools for safeguarding critical infrastructure.
Research Focus
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Top Papers
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