Jie Zhou
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Researcher
Graduate Student Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Jie Zhou's research focuses on the development of scalable parametric approaches for outlier detection in high-dimensional datasets. Their work, including a 2025 publication titled "Distribution-Aware Outlier Detection in High Dimensions: A Scalable Parametric Approach," investigates methods to identify anomalous data points within complex, multi-dimensional datasets. This research has the potential to improve data analysis and anomaly detection in various fields that rely on large datasets. Zhou has collaborated with Weiqiang Dong from Southern Arkansas University, co-authoring one publication.
Metrics
- Publications: 1
Selected Publications
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Distribution-Aware Outlier Detection in High Dimensions: A Scalable Parametric Approach (2025)
Collaboration Network
Top Collaborators
- Distribution-Aware Outlier Detection in High Dimensions: A Scalable Parametric Approach
- Distribution-Aware Outlier Detection in High Dimensions: A Scalable Parametric Approach
- Distribution-Aware Outlier Detection in High Dimensions: A Scalable Parametric Approach
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