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.
We are looking for application level and tool-centric talks and tutorials on the following topics:
- Deep Learning
- Text Mining
- Computer Vision
- Social Network Analysis
- Large-scale Machine Learning (ML)
- Internet of Things (IoT)
- Computational Biology
- ML in healthcare
- ML in education
- ML in energy and ecology
- ML in agriculrure
- Analytics for emerging markets
- ML in e-governance
- ML in smart cities
- ML in defense
The deadline for submitting proposals is 30th April 2016
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.
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
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
ML in fin-tech - Transforming 60 crore Indian lives
I lead Finomena, which uses the power of big-data, AI and ML in every imaginable way (information retrieval, NLP, deep learning, social network analysis, fraud detection and prevention, image recognition (even from videos), speech to text transcription and analysis, reinforcement learning) on a daily basis to provide access to credit to people in the long tail in India - over 60 crore people who would otherwise be rejected by any Bank or Financial Institution.
This talk will describe the massive access to credit problem in India, (or why fin-tech is the hottest kid on the block today), and open the audience’s minds up to using their ML knowledge towards this cause which is improving how people live their lives everyday. We can enable them a better quality of life, all while appreciating their unique differences to personalise the risk-assessment and risk-pricing, and still being able to scale up the technology using ML.
“Small tenure, small ticket-size” loans is a genre which cannot be solved by traditional ways of risk-assessment in banking. The cost of sourcing, analysing, approving and servicing is way too high for a request for a Rs. 60,000 laptop. And if that request is from a student, it will be rejected outright due to no income and no credit history. So the entire process has to be re-thought end-to-end from first-principles to be non-traditional and technology-first. Technology has to deeply disrupt every stage, so that disbursing loans at scale for such small amounts becomes viable.
As the former Economic Advisor to the Government of India C. Rangarajan recently said, “There are two aspects to financial inclusion: one is bank accounts and the second is access to credit. The scheme announced by the prime minister addresses the first problem. The issue of making credit available to small borrowers remains.”
ML in fin-tech is helping bring Financial Inclusion in India at the biggest scale seen in history so far. Character evaluation and risk-assessment still remain extremely complex areas as they are about evaluating how people might behave. There is plenty of behavioral psychology to take advantage of as well. We capture 20,000+ data points during the application process, and use those to varying degrees in the evaluation.
- Proof that Banking is the most data-intensive business in the world!
- How will big-data help increase penetration of credit in India
- How to increase penetration of credit in India
- The Long tail
- Using big-data to personalise
- The future of Holistic Risk-assessment and Differentiated risk-pricing
- A sneak-peak into our Credit-scoring-engine architecture
- Examples of how to use AI and ML in every imaginable way to provide access to credit to people in the long tail in India - over 60 crore people who would otherwise be rejected by any Bank or Financial Institution
- E.g. image recognition
- information retrieval and NLP
- deep learning
- social network analysis
- fraud detection and prevention
- The importance and impact of Re-inforcement learning
- Examples of why ML is harder on these ever-evolving dynamic datasets.
An open mind, and excitement! :)
- I studied BS and MS at Stanford University in Computer Science, built GraphSearch with the creator of Google Maps Lars while working as a Facebook engineer, was a VC associate at MDV ($700M fund) post that, and the youngest PM at Microsoft HoloLens post that, among other experiences. My two specialisations at Stanford were Systems and AI. I also enjoy behavioral psychology and we’re employing it in our product, design, and risk-assessment algorithms.
- I am co-leading the largest consumer-lending fin-tech company in India:
- Fin-tech in India is a trillion$ market because of India’s size. Today we have 25 crore people online and in the next 5 years that number is poised to grow to around 70-80 crore. This kind of a relative and absolute growth will not be witnessed again in the world
- India has ~53% of its population under 25 years of age. It is a country of millennials, for whom, banking and lending have to be re-imagined.
- India is mobile-first.
- Finomena, the company I co-lead, is bringing Financial Inclusion to a country of 1.25 Billion people through focusing on the holy-grail of “small tenure, small ticket size” lending which can only be done in a tech-first way to be effective.
- Finomena is built on the foundation provided by Aadhaar (the world’s largest biometric fingerprint and identity platform of it’s kind with 1 Billion people already registered on it). The Aadhaar platform will be as revolutionary over the next 5 years as the smartphone platform was from 2010-2015 in India.
- I am a contributor to the “India-Stack” discussions - this refers to the most advanced fin-tech infrastructure in the world being developed by the Indian govt and several leading private sector thought-leaders in India
- India is it’s own beast (because of it’s scale, diversity and chaos), and requires it’s own innovations, and I am in the rare position to compare and contrast the happenings in India with those in US (because I spent 7 years there) and/or China today.
- Slides: https://drive.google.com/file/d/0BzPOe_Rk4qTENGdncTg1MzVBQkk/view?usp=sharing
- TedX video: https://www.youtube.com/watch?v=no0ykygcra8&list=PLOLAMSA6_Ihj9GmYJ7rOrRCtVgeoSImMt&index=8
- (Total video length ~25mins. Please start from 3:10. Before that my mic wasn’t working)
- Spoke at TedX IIT Delhi in Feb 2016, and had the task of presenting to an audience (which might or might not know or care about fin-tech), how big-data can help increase the penetration of credit in India, in ways only tech-first (not tech-enabled) fin-tech companies can execute. We also deconstructed the buzz-word “big-data” from first-principles in order to explain our point.
- Blog post: https://finomena.com/blog/why_finomena
- A link to my Linkedin page:- http://www.linkedin.com/in/riddhimittal