Proposals

The Fifth Elephant 2020 edition

On data governance, engineering for data privacy and data science

Proposal guidelines

For details about The Fifth Elephant, see: https://hasgeek.com/fifthelephant/2020/#about

The Fifth Elephant will cover the following topics, and more:

  1. Data governance
  2. Data privacy
  3. Engineering for data privacy
  4. Engineering for Personal Data Protection (PDP) Bill
  5. Data annotation, labelling and overall health of data
  6. Feature engineering and ML platforms
  7. ML engineering
  8. Collaboration between data science and data engineering teams.
  9. Productionizing data science.

We invite communities to collaborate with us to curate tracks, sessions and meetups at the conference.

Participant profile at The Fifth Elephant

Speakers like to know who will be in their audience and therefore how to prepare talks. Below is a list of potential participants at The Fifth Elephant:

  1. ML engineers
  2. Data scientists
  3. Data engineers
  4. Privacy engineers
  5. Lawyers and legal researchers working on data privacy
  6. Business unit heads
  7. Product managers

Session formats

  1. Full talks - 40 mins duration
  2. Crisp talks - 20 mins duration
  3. Flash talks - 5-10 mins
  4. Birds of Feather (BOF) session - 1 hour duration
  5. Round tables - 1-3 hours duration
  6. Hands-on workshops, where participants follow instructors on their laptops: 3-6 hours duration
  7. Suggest your own format

Selection process

Proposals will be accepted based on the themes for The Fifth Elephant and topics which participants propose.

The schedule will be announced iteratively. You can propose sessions for speakers to speak based on topics and ideas of your interet.

We recommend that proposers do the following with/after submitting proposals:

  1. Add links to videos/slide decks if your talk is at an advanced stage of articulation.
  2. Explain problem statement and the key learnings in greater detail.
  3. Submit your proposal early for feedback and review.

The Fifth Elephant’s policy is one speaker per talk.

Passes and honorarium for speakers

We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and a discount code which they can share with their colleagues, communities they are part of, and on social media channels. We do not provide free passes for speakers’ colleagues and spouses. Please do not ask us for this.

Travel grants for outstation speakers:

Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans.
If you require a grant, request it when you submit your proposal in the field where you add your location. Rootconf is funded through ticket purchases and sponsorships; travel grant budgets vary.

Important dates:

Last date for submissions: 31 May, 2020

Conference dates: 19-20 June 2020

Schedule announcement: 30 April 2020 onwards

Contact:
Write to fifthelephant.editorial@hasgeek.com if you have questions regarding talks/sessions at the conference.

Submit a session proposal

Submissions are closed for this project

All proposals

Confirmed sessions

The Akoma Ntoso document standard for legislative and judicial documents

Ashok Hariharan (@bungeni-ashok)

  • 0 comments
  • Wed, 04 Mar

Privacy Law-Aware ML Data Preparation

Venkata Pingali (@pingali)

  • 0 comments
  • Mon, 17 Feb

Unconfirmed proposals

Predicting Deal Closure in a Sales CRM using Email Sentiment

Vishal Gupta (@vizgupta)

  • 0 comments
  • Sun, 31 May

Discover, Classify and Protect Enterprise Data with Deep Learning and NLP

Sanghamitra Bhattacharjee (@sanghamitrab)

  • 0 comments
  • Sun, 31 May

Making the ART of data science to SCIENCE again

Alok Kumar (@kumaralok)

  • 0 comments
  • Sun, 31 May

Taming the Data Elephant (aka) Productionizing Data Science!

Srimathi H (@shrimats)

  • 0 comments
  • Sun, 31 May

COVID Impact Analysis on people commute and places of visits leveraging Behaviour Analytics Models

Sheik Dawood (@sheikk-k)

  • 0 comments
  • Sun, 31 May

Error tolerant document retrieval in Autosuggest

Suryakant Pandey (@suryakantpandey)

  • 0 comments
  • Sun, 31 May

Finding high propensity users for Delivery Jobs

Imaad Mohamed Khan (@imaadmkhan1)

  • 0 comments
  • Sun, 31 May

PICASA: Predictive Interventions in Capacity Allocations through Systemic Automations

Gowtham Bellala (@gowthambellala)

  • 0 comments
  • Sun, 31 May

Challenges of understanding people’s places of visits using unsupervised geospatial techniques

Sayan Biswas (@sayanbiswas)

  • 0 comments
  • Sun, 31 May

Solving for Bias In E-Commerce Autosuggest

Pranjal Sanjanwala (@pranjalsanjanwala)

  • 0 comments
  • Sat, 30 May

Contextual Autocomplete suggestions in Realtime

Dileep Patchigolla (@dileep31)

  • 0 comments
  • Fri, 29 May

Bayesian Sampling

Shreya Jain (@shreya-jain)

  • 0 comments
  • Fri, 29 May

A Scalable Alternative to postgis in a Distributed Environment

Vishal Verma (@vishalzendrive)

  • 0 comments
  • Fri, 29 May

Scale Search Infrastructure with Apache Solr and Kubernetes

Amrit Sarkar (@sarkaramrit2)

  • 0 comments
  • Thu, 28 May

Case Study - Information Retrieval from millions of legal documents using Deep Learning models

Santosh

  • 0 comments
  • Wed, 27 May

Available != Usable. How public data lakes can accelerate drug discovery

Shashank Jatav (@shashj)

  • 0 comments
  • Thu, 21 May

Context Aware Autocomplete at Scale at Flipkart

krishan goyal (@krishan1390)

  • 0 comments
  • Thu, 21 May

Applied Data Science To Disrupt Medical Workstream

swayam mittal (@swayammittal65)

  • 0 comments
  • Fri, 15 May

Automatic Collision Notification (ACN): A Smartphone Based Crash Detection Technology

Arnab Chakraborty (@arnabchak)

  • 0 comments
  • Thu, 09 Apr

Detecting & Addressing Out of Distribution Data (OOD) Issues in Production ML Systems

Saravanan Chidambaram (@sarochida)

  • 0 comments
  • Wed, 25 Mar

Is Your NLP Model Solving the Dataset Or the Actual Task? - Identifying, Analyzing and Mitigating Spurious Dataset Cues in NLP Applications

Sandya Mannarswamy (@sandyasm)

  • 0 comments
  • Tue, 24 Mar

Developing a match-making algorithm between customers and Go-Jek products!

Gunjan Dewan (@gunjandewan)

  • 0 comments
  • Wed, 26 Feb

Privacy Preserving AI: Protecting User Privacy without Compromising Quality of Service

upendra singh (@upendrasingh1)

  • 0 comments
  • Wed, 19 Feb

Social Engineering (The Dark Side Of Tech)

CRUX CONCEPTION

  • 1 comments
  • Tue, 11 Feb