Track: Machine Learning 2.0

Location: Majestic Complex, 6th fl

Day of week: Wednesday

Machine Learning has made our lives more productive from hailing a ride via Uber’s advanced ML-driven rider and driver matching, or Google Now predicting information you’d need before you need it. Machine learning has also made our lives safer allowing people to rent strangers’ houses via Airbnb or reducing the risk of fraud during online purchases. Recent advances in deep learning have brought more new technologies within our reach including self-driving cars, machine translation, predicting weather several years ahead, automated stock trading and more! In this track, come hear from practitioners about some interesting applications of machine learning and recent practical advances in deep learning.

Track Host:
Soups Ranjan
Director of Data Science @Coinbase

Soups Ranjan is the Director of Data Science at Coinbase, one the largest bitcoin exchanges in the world. He manages the Risk & Data Science team that is chartered with preventing avoidable losses to the company due to payment fraud or account takeovers. Soups has a PhD in ECE on network security from Rice University. He has previously led the development of Machine Learning pipelines to improve performance advertising at Yelp and Flurry. He is the founder of, a round-table forum for risk professionals in San Francisco to share ideas on stopping bad actors.

10:35am - 11:25am

by Prabhat
Data and Analytics Group Lead @NERSC

Climate change is one of the most important problems facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to understand the evolution of the climate system subject to various CO2 emission scenarios. Large scale climate simulations produce 100TB-sized spatio-temporal, multi-variate datasets, making it difficult to conduct sophisticated analytics. In this talk, I will present our results in applying Deep Learning for supervised and semi-supervised...

11:50am - 12:40pm

by Nelson Ray
Data Scientist @Opendoor

American homes represent a $25 trillion asset class, with very little liquidity. Selling a home on the market takes months of hassle and uncertainty. Opendoor offers to buy houses from sellers, charging a fee for this service. Opendoor bears the risk in reselling the house and needs to understand the effectiveness of different hazard-based liquidity models.

This talk focuses on how to estimate the business impact of launching various machine learning models, in particular, those we...

1:40pm - 2:30pm

by Soups Ranjan
Director of Data Science @Coinbase

Coinbase is the one of the largest digital currency exchanges in the world. We store about $1B of digital currency (bitcoin, litecoin, ether) on behalf of our users. Given the instant nature of digital currency and that it can't be reversed, we have one of the hardest payment fraud and security problems in the world. We are hit by the most sophisticated scammers constantly and consequently we are at the forefront of the fight against fraudsters and hackers. We've witnessed and solved...

2:55pm - 3:45pm

by Sally Langford
Data Scientist @StitchFix

Stitch Fix is a personalized styling service for clothing and accessories. Items are selected by a stylist from initial personalized recommendations. The client keeps the items they love, and returns the rest, while providing detailed feedback. We use machine learning and analytics to power every aspect of our business from personalized recommendations, to inventory management and demand modeling.

In this talk, I’ll focus on the use of machine learning within our inventory forecasting...

4:10pm - 5:00pm

by John Langford
Leading Machine Learning Researcher, Vowpal Wabbit Contributor

by Markus Cozowicz
Senior Research Software Development Engineer @Microsoft

We have been on a decade long quest to make online interactive learning, a routine fact of life for programmers everywhere. Online learning systems can react quickly to changes in behaviour and have wide ranging applications such as fraud detection, advertising click-through rate prediction, etc. Doing this well has required fundamental research and development of one of the most popular open-source online learning algorithm systems ( ). Most recently, we have also...


Monday, 26 June

Tuesday, 27 June

Wednesday, 28 June