Publications

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

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

December 17, 2018

SIAM Journal on Matrix Analysis and Applications

Linear least-squares regression with a “design” matrix A approximates a given matrix B via minimization of the spectral- or Frobenius-norm discrepancy ||AX − B|| over every conformingly sized matrix X. Also popular is low-rank approximation to B through the “interpolative decomposition,” which traditionally has no supervision from any auxiliary matrix A.

Areas: Data Science Facebook AI Research Machine Learning

December 11, 2018

International Conference on Complex Networks and their Applications

Product-adoption scenarios are often theoretically modeled as “influence-maximization” (IM) problems, where people influence one another to adopt and the goal is to find a limited set of people to “seed” so as to maximize long-term adoption. In many IM models, if there is no budgetary limit on seeding, the optimal approach involves seeding everybody immediately. Here, we argue that this approach can lead to suboptimal outcomes for “social products” that allow people to communicate with one another.

Areas: Data Science Facebook AI Research

November 7, 2018

Journal of Economic Perspectives

Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors.

Areas: Data Science Economics & Computation

November 3, 2018

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

This work is the first of its kind, examining these phenomena across three major disasters in the United States—Hurricane Harvey, Hurricane Irma, and Hurricane Maria—using aggregated, de-identified data from over 1.5 million Facebook users.

Areas: Data Science

November 3, 2018

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

Like sighted people, visually impaired people want to share photographs on social networking services, but find it difficult to identify and select photos from their albums. We aimed to address this problem by incorporating state-of-the-art computer-generated descriptions into Facebook’s photo-sharing feature.

Areas: Data Science Human Computer Interaction & UX

October 9, 2018

Solar-powered high-altitude long endurance aircraft that harvest and store solar energy can fly indefinitely if they are able to close a 24-hour energy cycle. Perpetual endurance is possible when energy consumption does not exceed energy storage.

Areas: Connectivity Data Science Economics & Computation

August 19, 2018

Knowledge Discovery in Databases (KDD)

This paper describes the end-to-end regression detection system designed and used at Facebook. The main detection algorithm is based on sequential statistics supplemented by signal processing transformations, and the performance of the algorithm was assessed with a mixture of online and offline tests across different use cases.

Areas: Data Science Facebook AI Research Systems & Networking

August 16, 2018

Bayesian Analysis 2018

We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized.

Areas: Data Science Machine Learning