Theme and format
The Fifth Elephant 2017 is a four-track conference on:
- Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
- Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
- Hands-on tutorials on data mining tools, and ML platforms and techniques.
- Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
- If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.
About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
- Full-length, 40-minute talks.
- Crisp, 15-minute talks.
- Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
- Off-the-record (OTR) sessions of 60-90 minutes duration.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- Proposers are also encouraged to vote and comment on other proposals submitted here.
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or 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.
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for 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), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
- Deadline for submitting proposals: June 10
- First draft of the coference schedule: June 20
- Tutorial and workshop announcements: June 20
- Final conference schedule: July 5
- Conference dates: 27-28 July
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
Designing Machine Learning Pipelines for Mining Transactional SMS Messages
Much of data science involves using data for some practical, business purpose. The data usually needs to be cleaned and processed and that might take a while, but it is generally close to where it needs to be. It can be incredibly exciting and engaging to work at one level back, where data is far from where it needs to be. At this level real work has to be done to transform data into a form ready to be turned into value. In this talk I will walk through this process with the example of turning raw transactional SMS text into structured personal finance features.
The work involves several steps of interest that I will present in the talk. It begins with creatively reducing and representing financial life (or any domain) with concise and clearly defined features. This is a step often overlooked in the rush to fit models and if not done well is most likely to ensure the work fails. Next is taking a difficult composite problem and breaking it into pieces. As Max Tegmark put it: “If you have a tough question that you can’t answer, first tackle a simpler question that you can’t answer.” I’ll show how machine-learning models can be combined in sequence to progressively build up structure and refine raw text into meaningful pieces of information.
While illustrating each step in the work with concrete examples (with code usually written in Python), I will focus on the generalizable process of designing and building machine learning pipelines and transforming raw data into features (using programming language/framework of choice).
Background on problem and data:
The government of India has a regulatory requirement of two-factor authentication for digital financial transactions. To remain compliant with this requirement, companies and organizations usually require a user’s mobile phone number. And as a result it is common for people to receive SMS notifications for their banking and digital service transactions.
These SMS messages are semi-structured short text which can be conceptually broken into two pieces: 1) a template structure and 2) variables within that template. Each SMS-sending organization (including hundreds of individual banks across the country) has its own message templates and usually multiple different templates for any given message type (e.g. notification of a debit transaction).
1) Introduction to data science problems where transforming raw data into features itelf requires statistical modeling and the example case of transactional SMS messages.
2) The creative process of representing a complex domain of the world (like personal finance) with concepts and features that can be mapped to data.
3) Building machine learning models to solve the problems defined in step 2 and designing an architecture to run the models in extensible and auditable pipelines.
*/ Each part of the talk will include detailed examples and technical content */
Paul Meinshausen is a globally experienced data scientist. He is a Co-Founder of PaySense, a mobile fintech startup based in Mumbai, and was Chief Data Officer at the company until February 2017. Before co-founding PaySense, Paul was Vice President of Data Science at Housing.com, where he led the Data Science Lab and the Product Analytics and Business Intelligence teams. Earlier he was Principal Data Scientist at Teradata, where he worked on machine learning projects in the Banking, Telecom, Automotive, and E-Commerce industries across the South Asia and APAC regions. Paul was a Data Science for Social Good Fellow at the Computation Institute at the University of Chicago in 2013. Between 2009 and 2011 he served as an analyst for the U.S. Department of the Army and deployed to Kabul, Afghanistan to the headquarters of the International Security Assistance Force in Afghanistan. Paul has an academic research background in behavioral science and was a researcher in the Department of Psychology at Harvard University and a Fulbright Scholar in Turkey at the Middle East Technical University.