Ori Stitelman
Papers
1
Total Citations
25
H-Index
1
About
Ori Stitelman is a leading researcher in computational advertising, machine learning, and causal inference, with a particular focus on the integrity and reliability of online data systems. His most cited work, "Using co-visitation networks for detecting large scale online display advertising exchange fraud" (2013, 25 citations), addresses a critical challenge in the digital advertising ecosystem: the detection of fraudulent traffic in real-time bidding exchanges. By leveraging co-visitation networks—graphs built from the observed browsing patterns of web users—Stitelman developed a scalable, unsupervised method to uncover coordinated, non-human behavior that inflates ad impressions and clicks. This contribution not only advanced the field of anomaly detection in high-dimensional, streaming data but also provided a practical tool for protecting billions of dollars in ad spend. Stitelman’s research sits at the intersection of applied statistics and industry-scale data engineering, demonstrating how causal reasoning and network analysis can safeguard the integrity of online markets. His work has been influential in both academic circles and industry practice, with the 2013 paper serving as a foundational reference for subsequent research on ad fraud detection and the broader study of adversarial behavior in large-scale online platforms.
Research Focus
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Top Papers
- 1