The Fifth Elephant 2016

India's most renowned data science conference

The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.

Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.

We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.

#Tracks

We are looking for application level and tool-centric talks and tutorials on the following topics:

  1. Deep Learning
  2. Text Mining
  3. Computer Vision
  4. Social Network Analysis
  5. Large-scale Machine Learning (ML)
  6. Internet of Things (IoT)
  7. Computational Biology
  8. ML in healthcare
  9. ML in education
  10. ML in energy and ecology
  11. ML in agriculrure
  12. Analytics for emerging markets
  13. ML in e-governance
  14. ML in smart cities
  15. ML in defense

The deadline for submitting proposals is 30th April 2016

Format

This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:

  • Full-length 40 minute talks.
  • Crisp 15-minute talks.
  • Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on Workshop sessions, 3 and 6 hour duration.

Selection process

Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.

We expect you to submit an outline of your proposed talk – either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal.

We will notify you about the status of your proposal within three weeks of submission.

Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).

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 licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise 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.

Key dates and deadlines

  • Revised paper submission deadline: 17 June 2016
  • Confirmed talks announcement (in batches): 13 June 2016
  • Schedule announcement: 30 June 2016
  • Conference dates: 28-29 July 2016

##Venue
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.

##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Anubhav Dikshit

Machine Learning Application in MicroFinance

Submitted Apr 30, 2016

Artoo is a loan origination system (LOS), our aim is to improve the financial inclusion in world (starting with India). As a testament to our mission we have help disbursed 1 Lac loans worth 1,000 crores (last two years), we wanted to share our experience of using data and eventually data science in helping our clients take the right call while disbursing loans.

Outline

MFI in India is one of the most heavily regulated industry, these industries cater to customer who are deemed ‘high risk customers’ by traditional banks. The other alternative to these ‘high risk customers’ are the unregulated loan sharks. The loans given out by these MFI’s tend to have interest rates of 20% and upward at and thus there is a high reward (both to customer and the MFI itself) to reduce operational cost.
Due to the nature of the customer (undocumented income, with no credit history), traditional process to gauge a customer does not suffice (credit bureau report). In order to inspire enough confidence about a customer Artoo captures 800 odd data points about the customer (all captured in the field).
Its Artoo’s belief that, if we help MFI’s become more efficient, accurate and quicker, then this will lead to MFI(s) will in turn reach more people and increasing the financial inclusion. In order to quicken the loan disbursement process during credit underwriting process Artoo points only the data points that are off for a given occupation and geography (income is 25% higher than the trend, household expense much lower than the trend).

Requirements

Nothing

Speaker bio

An analytics & machine learning enthusiast with experience of close to 2 years. Worked as a field force division for a US based pharmaceutical company, after working for a span of 14 months I joined Artoo a Bangalore Fintech based startup.

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Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more