Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore
Arun Kejariwal

Arun Kejariwal
Statistical Learning Principal, MZ

@arun_kejariwal

Arun Kejariwal is a statistical learning principal at Machine Zone (MZ), where he leads a team of top-tier researchers and works 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 is building 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

4:00pm4:30pm Tuesday, December 5, 2017
Smart cities and urban automation
Location: 323 Level: Intermediate
One of the key application domains leveraging live data is smart cities, but success depends on the availability of generic platforms that support high throughput and ultralow latency. Arun Kejariwa and Francois Orsini offer an overview of Satori's live data platform and walk you through a country-scale case study of its implementation. Read more.
11:15am11:55am Wednesday, December 6, 2017
Machine Learning
Location: 323
Anomalies occur frequently in live data for a multitude of reasons, so detection and filtering of anomalies is of paramount importance for robust decision making. Dhruv Choudhary, Arun Kejariwal, and Francois Orsini explore the design and architecture of MZ's Satori platform and share techniques for anomaly detection on live data. Read more.