Build resilient systems at scale
May 27–29, 2015 • Santa Clara, CA
Arun Kejariwal

Arun Kejariwal
Statistical Learning Principal, MZ

@arun_kejariwal

@arun_kejariwal is a software engineer at Twitter, where he works on research and development of novel techniques for time series analysis. Prior to joining Twitter, Arun worked on research and development of practical and statistically rigorous methodologies to deliver high performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been published in peer-reviewed international conferences and journals.

Arun received his Bachelor’s degree in EE from IIT Delhi and doctorate in CS from UCI.

Sessions

1:45pm–2:25pm Thursday, 05/28/2015
Location: Ballroom CD
Arun Kejariwal (MZ), Sailesh Mittal (Twitter), Karthik Ramasamy (Streamlio)
Average rating: **...
(2.95, 20 ratings)
Data-driven decision making rests, in part, on availability of high fidelity data. Presence of anomalies limits the use of data on an “as is” basis. Automatic anomaly detection is key to providing high fidelity data. We present a statistically rigorous method for automatic anomaly detection, which leverages correlations between multiple time series. Read more.