Conference: Jun 26-28, 2017
Workshops: Jun 29-30, 2017
Track: Machine Learning 2.0
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.
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 RiskSalon.org, a round-table forum for risk professionals in San Francisco to share ideas on stopping bad actors.
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...
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...
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...
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...
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 (http://hunch.net/~vw ). Most recently, we have also...
Tracks
Monday, 26 June
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Microservices: Patterns & Practices
Practical experiences and lessons with Microservices.
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Java - Propelling the Ecosystem Forward
Lessons from Java 8, prepping for Java 9, and looking ahead at Java 10. Innovators in Java.
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High Velocity Dev Teams
Working Smarter as a team. Improving value delivery of engineers. Lean and Agile principles.
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Modern Browser-Based Apps
Reactive, cross platform, progressive - webapp tech today.
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Innovations in Fintech
Technology, tools and techniques supporting modern financial services.
Tuesday, 27 June
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Architectures You've Always Wondered About
Case studies from the most relevant names in software.
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Developer Experience: Level up Your Engineering Effectiveness
Trends, tools and projects that we're using to maximally empower your developers.
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Chaos & Resilience
Failures, edge cases and how we're embracing them.
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Stream Processing at Large
Rapidly moving data at scale.
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Building Security Infrastructure
How our industry is being attacked and what you can do about it.
Wednesday, 28 June
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Next Gen APIs: Designs, Protocols, and Evolution
Practical deep-dives into public and internal API design, tooling and techniques for evolving them, and binary and graph-based protocols.
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Immutable Infrastructures: Orchestration, Serverless, and More
What's next in infrastructure. How cloud function like lambda are making their way into production.
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Machine Learning 2.0
Machine Learning 2.0, Deep Learning & Deep Learning Datasets.
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Modern CS in the Real World
Applied, practical, & real-world dive into industry adoption of modern CS.
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Optimizing Yourself
Maximizing your impact as an engineer, as a leader, and as a person.
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Ask Me Anything (AMA)
