Thursday 28 July 2016
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:30–09:20
Check-in and breakfast
09:20–09:30
Introduction to The Fifth Elephant 2016
09:30–10:30
Using Data to Identify the Genomic Cause of Disease
Ramesh Hariharan, Strand Life Sciences
09:35–10:20
Allocation and Forecasting in Guaranteed Delivery of Advertisements
Aditya Ramana Rachakonda, Flipkart
10:30–11:30
Keynote: The Alternative Data revolution on Wall St
Gene Ekster, Analyst and Investment Consultant
11:30–12:00
Morning tea break
12:00–12:45
ML in fin-tech - Transforming 60 crore Indian lives
Riddhi Mittal, Finomena
12:00–12:45
Purpose, Speed & Visibility : Facilitating product discovery & engagement on a e-commerce website
Ekta Grover, BloomReach Inc
12:45–13:30
Deciphering Driving Behaviour using Geospatial Temporal Data Collected from Smartphone Sensors
Aditya Karnik, Zendrive
12:45–13:30
Taking Fashion and Lifestyle Commerce Towards SKUs Using Deep Image and Text Parsing
Vijay Gabale, Infilect
13:30–14:40
Lunch
14:40–15:00
Data-Driven Decision Making in Indian Agriculture: the Present and the Future
Udit Poddar, SocialCops
14:40–15:00
RightFit- A Data Science Approach to Reduce Product Returns in Fashion e-Commerce
Ashish Kulkarni, JabongLabs
15:00–16:00
What do machine learning and high performance computing have to do with big cats in the wild?
Dr. Arjun Gopalaswamy, Indian Statistical Institute, Bangalore
15:00–15:45
Scaling the Largest Functional DataSet @Flipkart aka Catalog
Anuj Mittal, Flipkart
15:00–16:00
Birds of Feather (BoF): How much math and statistics programmers need to know to hack their way into Machine Learning?
Facilitators: Amit Kapoor, Bargava S., Ashwin Kumar
15:45–16:05
Increasing Trust and Efficiency of Data Science using dataset versioning
Venkata Pingali, Scribble Data
16:00–16:05
Birds of Feather (BoF): Alternative Data
Facilitators: Gene Ekster, Udit Poddar
16:05–16:40
Evening tea break
16:40–17:25
Hierarchical Structure, Hierarchical Bayes approach and implementation of MCMC
Soumen Dey, Indian Statistical Institute, Bangalore
16:40–17:25
Taking Analytics Applications from Labs to the Real World: Transfer Learning in Practice
Shourya Roy, Xerox Research
17:25–18:25
Djembe Jam
Friday 29 July 2016
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:30–09:20
Check-in and breakfast
09:20–09:30
Summary of day 1
10:15–11:00
Meet the needs of content marketing with the power of NLP
Balaji Vasan, Adobe Research Big Data Experience Labs
10:15–10:45
Let your Big Data Processing take flight with Apache Falcon
Pallavi Rao, InMobi
10:45–11:05
Timely Dataflow
Bharani S., ThoughtWorks
11:05–11:35
Morning tea break
11:35–12:35
Keynote: Reasoning – The Next Frontier in Data Science
Shailesh Kumar, ThirdLeap
12:35–12:55
Lessons Learned : Real-life NLP
Martin Andrews, Cambridge Business Solutions
12:35–12:55
Looking under the hood - demystifying data tools
Simrat Hanspal, Mad Street Den
12:55–14:10
Lunch
14:10–14:55
Continuous online learning for classification tasks
Saurabh Arora, Freshdesk
14:10–14:55
Scalable Realtime Analytics using Druid
Nishant Bangarwa, HortonWorks
14:55–15:40
Convolutional Neural Networks from the Other Side
Sumod Mohan, SolitonTech
14:55–15:25
Dr. Elephant - Self-Serve Performance Tuning for Hadoop and Spark
Akshay Rai, LinkedIn
15:40–16:20
Evening tea break
16:20–17:05
Model Visualisation
Amit Kapoor, NarrativeViz
16:20–16:45
Hadoop & Cloud Storage: Object Store Integration in Production
Rajesh Balamohan, HortonWorks
16:20–16:55
Birds of Feather (BoF): From Machine Learning to Deep Learning – when, how and why do you make the transition?
Facilitators: Sumod Mohan, Martin Andrews, Prasanna S, Shailesh Kumar
16:45–17:10
Machine Learning the Walmart Way with a Deep Dive into Automated Forecasting System
Anindya Sankar Dey, @WalmartLabs
17:05–17:50
Real Time Fulfilment Planning at Flipkart Scale
Jagadeesh Huliyar, Flipkart
17:10–17:30
Logging at scale using Graylog - Billion+ messages, 100K req/sec
Rohit Gupta
17:30–17:55
Reducing the world with JavaScript
Aruna S, Mapbox
17:55–18:15
Feedback and theme for 2017
Saturday 30 July 2016
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:45–09:30
Check-in and Breakfast
09:30–11:00
Advanced Deep Learning Workshop – Hands-on
Martin Andrews
09:30–11:00
Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
Bargava Subramanian
11:00–11:15
Tea Break
11:15–12:45
Contd. Advanced Deep Learning Workshop – Hands-on
11:15–12:45
Contd. Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
12:45–13:45
Lunch
13:45–15:30
Contd. Advanced Deep Learning Workshop – Hands-on
13:45–15:30
Contd. Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
15:30–15:45
Tea Break
15:45–17:00
Contd. Advanced Deep Learning Workshop – Hands-on
15:45–17:00
Contd. Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
Sunday 31 July 2016
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:15–09:00
Check-in and Breakfast
09:00–11:00
Deep Learning for Computer Vision
Hari C M
11:00–11:15
Tea Break
11:15–13:15
Contd. Deep Learning for Computer Vision
13:15–14:15
Lunch
14:15–16:00
Building a scalable Data Science Platform ( Luigi, Apache Spark, Pandas, Flask)
Nischal HP
16:00–16:15
Tea Break
16:15–18:00
Contd. Building a scalable Data Science Platform ( Luigi, Apache Spark, Pandas, Flask)