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Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA
Arun Kejariwal

Arun Kejariwal
R&D Leader, Independent

@arun_kejariwal

Until recently, Arun Kejariwal was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open-sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high-performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Sessions

11:55am-12:35pm Thursday, September 6, 2018
Location: Continental 1-3
Secondary topics:  Deep Learning models, Temporal data and time-series
Ira Cohen (Anodot), Arun Kejariwal (Independent)
Average rating: *****
(5.00, 1 rating)
Ira Cohen shares a novel approach for building more reliable prediction models by integrating anomalies in them. Arun Kejariwal then walks you through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data. Read more.