The Fifth Elephant 2015

A conference on data, machine learning, and distributed and parallel computing

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.

Track 1: Discovering Insights and Driving Decisions

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.

Track 2: Speed at Scale

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

Commitment to Open Source

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.

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Paul Meinshausen


Developing a Hybrid Recommender System for Some of Life’s Most Important Choices

Submitted Jun 15, 2015

Recommender Systems are both an old and an active area of research. Advances in Recommender Systems can emerge from developing applications in new contexts and for new use cases. In this session we will describe the unique challenges associated with building a recommender system for real estate and we will present the work we are doing to develop a hybrid recommender system for real estate at


The recommendation problem in the property and real estate context has several domain specific characteristics which strongly influence the design and algorithmic approach for making recommendations to users. Some of the challenges of the real estate domain include perishable, metamorphic and rapidly changing inventory; constrained and ambivalent users with strongly conditional preferences; and the need to draw inferences about users’ preferences from infrequent transactions with little to no explicit user feedback. Some of the implications of these challenges are that content-filtering approaches are inappropriate and that collaborative filtering approaches are also incapable of providing a robust solution.

We will present an innovative approach we have developed at Housing’s Data Science Lab for modeling user preferences and a hybrid approach for making inferences about users’ interests.


There are no requirements for this session

Speaker bio

Vedavyas Chigurupati is a Data Scientist at He was previously working with SAP Labs India as a Software Developer. He has been playing an active and leading part in developing the recommender system at Housing.



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All about data science and machine learning