Machine Learning at Uber: Moving the world with data

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Join us at Uber for a series of talks about all the different ways that Uber uses machine learning & data science in their business. Snacks and drinks will be provided by Uber.

Note: Per the request of Uber, please add your first and last name to this form (http://t.uber.com/wimlds) for security purposes (it will help speed up security when you check in). If you are interested in hearing from Uber about job opportunities, you can leave your contact information in the form as well. They have a lot of open positions (https://www.uber.com/careers/list/?city=all&country=all&keywords=&subteam=data-science&team=engineering) on their data science team.

Title: Data Science for Product @ Uber

Speakers: Annie Tran (https://www.linkedin.com/in/annie-tran-1048769), JR New (https://www.linkedin.com/in/jrnew), Shuo Xie (https://www.linkedin.com/in/shuo-xie-april-a163a354)

Abstract: What is the role of data science in the product lifecycle at Uber? We will present three case studies on how data science informs Uber’s rider and driver products at various phases including initial feature ideation, building out a feature, integration with the core product, and further product optimization.

Title: Towards 99.99% Availability via Intelligent Real-time Monitoring

Speaker: Franziska Bell (https://www.linkedin.com/in/franziska-bell-phd-00097992)

Abstract: The Intelligent Real-time Monitoring team at Uber focuses on developing novel time series models for real-time outage and outlier detection. These models have broken new ground in detection accuracy and speed whilst being sufficiently computationally tractable to be applied to 100,000s of time series in real-time. This talk will give an overview of prerequisites, challenges and approaches to intelligent real-time monitoring.

Title: Uber and the Future of Urban Mobility

Speaker: Cory Kendrick (https://www.linkedin.com/in/coryhkendrick)

Abstract: At Uber, our mission is for transportation to be as reliable as running water – no matter where you are or where you're going, you can push a button and get a ride. I'll talk about how my team uses data to understand our impact on cities: from increased access and connectivity between neighborhoods, to how people use Uber to connect the last mile to public transportation, to mobility patterns throughout the day (including late-night), to how getting more people in fewer cars with uberPOOL can help reduce congestion on city streets.

Title: Sequential Testing Method for Experimentation

Speaker: Olivia Liao (https://www.linkedin.com/in/olivia-liao-3b847b20)

Abstract: Many A/B testing experimenters struggle between not making a "run until significance" mistakes and missing a runaway signal. The data usually doesn't arrive all at once and we want to monitor it as it comes in and try to call experiments early. We'll discuss the sequential testing method implemented in Uber's Experimentation Platform that allows us to do so while also relieving us from the frustration of fixing a sample size in advance.

Title: Uber’s Data Visualization Stack

Speaker: Shan He (https://www.linkedin.com/in/shan-he-25400b16)

Abstract: When creating graphics in the browser there’s no consensus on what language or API should be used. From markup languages like SVG to OpenGL based APIs like WebGL, the browser provides several different ways for creating visualizations. Through some examples @ Uber of both explanatory and high data-dense exploratory visualizations we’ll cover the available standards, libraries, and the open source stack we developed at Uber used for visual analytics, mapping and public-facing data visualizations ranging from SVG and D3 to WebGL and beyond.