Publications

July 15, 2019

ACM SIGIR Conference on Research and Development in Information Retrieval

Giving people the power to build community is central to Facebook’s mission. Technically, searching for communities poses very different challenges compared to the standard IR problems.

Areas: Data Science Facebook AI Research Machine Learning

June 22, 2019

MAPL at PLDI

This paper overviews the design and implementation choices for the HackPPL toolchain and presents findings by applying it to a representative problem faced by social media companies.

Areas: Data Science Machine Learning

June 13, 2019

International Conference on Machine Learning (ICML)

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by “irrelevant” aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data.

Areas: Data Science Economics & Computation Facebook AI Research

June 1, 2019

SIGKDD Explorations

To understand the top practical challenges in running OCEs at scale, representatives with experience in large-scale experimentation from thirteen different organizations (Airbnb, Amazon, Booking.com, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, Yandex, and Stanford University) were invited to the first Practical Online Controlled Experiments Summit. All thirteen organizations sent representatives.

Areas: Data Science

May 19, 2019

Conference on Information Systems for Crisis Response and Management (ISCRAM)

In this paper, we describe the data and methodology that power Facebook Disaster Maps. These maps utilize information about Facebook usage in areas impacted by natural hazards, producing aggregate pictures of how the population is affected by and responding to the hazard. The maps include insights into evacuations, cell network connectivity, access to electricity, and long-term displacement.

Areas: Connectivity Data Science

May 14, 2019

The Web Conference

In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to arrive at an accurate estimate.

Areas: Data Science Economics & Computation Facebook AI Research Machine Learning

May 4, 2019

ACM Conference on Human Factors in Computing Systems (CHI)

While many people use social network sites to connect with friends and family, some feel that their use is problematic, seriously affecting their sleep, work, or life. Pairing a survey of 20,000 Facebook users measuring perceptions of problematic use with behavioral and demographic data, we examined Facebook activities associated with problematic use as well as the kinds of people most likely to experience it.

Areas: Data Science Human Computer Interaction & UX

May 4, 2019

ACM CHI Conference on Human Factors in Computing Systems

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals’ general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups.

Areas: Data Science

April 12, 2019

IEEE International Conference on Data Engineering (ICDE)

Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible.

Areas: Data Science Systems & Networking

February 21, 2019

The Web Conference (WWW)

We present a method for implementing shrinkage of treatment effect estimators, and hence improving their precision, via experiment splitting.

Areas: Data Science Economics & Computation Machine Learning