Censored time-to-event data is critical to the proper modeling and understanding of customer engagement on the Uber platform. In this article, we demonstrate an easier way to model this data using Pyro.
In our ongoing series about rewriting the Uber driver app, engineer Kevin Babcock explains how we built the connection between the app and the Uber Beacon device, which displays a color remotely selected through a rider's app.
The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
In this article, Uber’s Marianne Borzic Ducournau discusses why financial planning at Uber presents unique and challenging opportunities for data scientists.
Ever wondered what it’s like to work in tech at Uber New York City? Just blocks from Times Square and Bryant Park, Uber’s new office in midtown Manhattan is home to more than a dozen teams, hundreds of employees (and growing), and a wide variety of engineering roles.
In its first year, the Career Prep Program, a collaboration between Uber and The Hidden Genius Project, demonstrated how technology-focused companies can embrace and reinforce values of diversity and inclusion among engineers, while having a positive impact in the community.
AresDB, Uber's open source real-time analytics engine, leverages GPUs to enable real-time computation and data processing in parallel.
Uber Labs utilizes insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for users on our platform.
Aarhus Engineering Internship: Building Aggregation Support for YQL, Uber’s Graph Query Language for Grail
Uber intern Lau Skorstengaard shares his experience working on YQL, the graph query language for our in-house infrastructure state aggregation platform.
Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.
In our ongoing series about rewriting the Uber driver app, engineer Chris Haugli explains how we designed the map display to be resilient, and always show the most useful information.
Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.
POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer
Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.
From Self-Driving Cars to Optimizing Claims Efficiency: My Unconventional Journey to Insurance Engineering
In this article, engineering manager Lili Kan reflects on her decision to lead Uber's Insurance Engineering team and discusses the challenges—and opportunities—of building insurance products for our platform.
Rather than shipping out our new driver app as a simple update to Android phones, Uber engineers delivered a dual binary package, enabling a safe and structured rollout of the new app while maintaining support for the previous version.
During an October 2018 meetup, members of our Women in Statistics, Data, Optimization, and Machine Learning (WiSDOM) group presented on their technical work at Uber.
For Uber's Profiles in Coding series, we interview Uber Freight engineer Sylvain Francois to find out the nature of his daily work and his best tips for coders.
The Uber Beacon leverages visual signaling, an accelerometer, and a gyroscope to improve the accuracy of in-app safety products like our automatic crash detection feature.