Put AI to Work
April 15-18, 2019
New York, NY
Arun Kejariwal

Arun Kejariwal
Lead Engineer, Independent

@arun_kejariwal

Arun Kejariwal is an independent lead engineer. Previously, he was he 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, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. 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

1:50pm–2:30pm Thursday, April 18, 2019
Deep learning for time series data
Machine Learning, Models and Methods
 Location: Grand Ballroom West
Secondary topics:  Financial Services, Models and Methods, Temporal data and time-series
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Average rating: ****.
(4.00, 3 ratings)
Arun Kejariwal and Ira Cohen share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data. Read more.