Arun Kejariwal
Lead Engineer, Independent

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

1:45pm–2:25pm Wednesday, September 11, 2019

Location: LL21 C/D

Sequence to sequence modeling for time series forecasting

Models and Methods

Secondary topics: Deep Learning, Machine Learning, Temporal data and time-series

Arun Kejariwal (Independent), Ira Cohen (Anodot)

Average rating:

(4.40, 5 ratings)

Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting. Read more.