BigData Experience Lab

Shriram Revankar

VP and Fellow, BigData Experience Lab

The BigData Experience Lab (BEL) aims to be a world class research lab focused on enhancing experience for Digital Marketing. Researchers from the lab employ big data analytics, content analysis and mobile intelligence techniques to enable organizations deliver delightful experiences. The lab created and delivered several break-through technologies, which deeply impacted Adobe products and became release defining features. The lab partners with the business units and engineering teams to shape the direction and future of several key Adobe products. The lab has attracted top talent to build two world class facilities in Bangalore and San Jose, and actively collaborates with the premier universities across the globe. The BEL also conducts a vibrant internship program where the best minds from top universities across the world get to explore futuristic ideas in greenfield areas. The lab has a proven track record of publishing papers with top tier conferences and journals, and helps Adobe build a strong intellectual property portfolio.

Areas of Research

Analytics and Data mining (Web, Social and Big data), Machine Learning and Pattern Recognition, Natural Language Processing and Computational Linguistics, Statistical Modelling and Inferencing, Information Retrieval, Large Scale Distributed Systems and Cloud Computing, Econometrics and Quantitative Marketing, Applied Game Theory and Mechanism Design, Operations Research and Optimization, Human Computer Interaction and Information Visualization

Latest BigData Experience Lab Publications

Harvesting Knowledge from Cultural Heritage Artifacts in Museums of India

Sancheti, A., Maheshwari, P., Chaturvedi, R., Monsy, A., Goyal, T., Srinivasan, B. (Jun. 3, 2018)
22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining

Modeling Time to Open of Emails with a Latent State for User Engagement Level

Sinha, M., Vinay, V., Singh, H. (Feb. 5, 2018)
ACM International Conference on Web Search and Data Mining (WSDM)

An LSTM Based System for Prediction of Human Activities with Durations

Krishna, K., Jain, D., Mehta, S., Choudhary, S. (Dec. 31, 2017)
To be presented at ACM UbiComp 2018 Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Rafiki: a middleware for parameter tuning of NoSQL datastores for dynamic metagenomics workloads

Mahgoub, A., Wood, P., Ganesh, S., Mitra, S., Gerlach, W., Harrison, T., Meyer, F., Grama, A., Bagchi, S., Chaterji, S. (Dec. 11, 2017)
Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference (Middleware '17)
See All Publications