Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Scalable real-time personalized recommendation system
This talk goes over some challenges in scaling a real time personalized recommendation system that can dynamically adapt to user actions and incorporate these signals into various applications like search, recommendations, predictive suggestions etc.
E-commerce applications these days are driven by personalization. And to provide these highly personalized user experiences, we need systems that track every user action and dynamically adapts sites’ search navigation and content uniquely to each user. This needs systems of massive scale that can process a large number of user events.
Bloomreach’s SNAP product personalizes site discovery for all consumers on any device or channel in real time to instinctively present the most relevant search results, left-navigation and contextual filters. This real time engine captures each individual user’s behaviour that further can be used to create cross device personalized search and navigation results that deliver true personalization to each user.
Jasvinder and Suchi worked on User data and personalization platform scaling for Bloomreach’s personalization service.
Jasvinder has a B.Tech, Computer Science from IIIT, Allahabad. He worked at Microsoft as a computer scientist for 5 years before joining Bloomreach.
Prior to Bloomreach, Suchi was a founding member of the Android framework team and has made extensive contributions to the Android framework.Suchi has an MS from University of Cincinnati and BE from BITS, Pilani. Her prior work experience includes working in companies like Google, Motorola and IBM Almaden.