Emma Pierson
Microsoft
65 Papers
296 Citations
Emma Pierson is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Population. The author has an hindex of 21, co-authored 48 publications. Previous affiliations of Emma Pierson include Cornell University & University of California, Berkeley.
Chat about Author
Papers
Mobility network models of COVID-19 explain inequities and inform reopening.
Serina Chang,Emma Pierson,Emma Pierson,Pang Wei Koh,Jaline Gerardin,Beth Redbird,David B. Grusky,Jure Leskovec +7 more
TL;DR: A metapopulation susceptible–exposed–infectious–removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas is introduced and correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups.
Algorithmic Decision Making and the Cost of Fairness
Sam Corbett-Davies,Emma Pierson,Avi Feller,Sharad Goel,Aziz Z. Huq +4 more
- 04 Aug 2017
TL;DR: This work reformulate algorithmic fairness as constrained optimization: the objective is to maximize public safety while satisfying formal fairness constraints designed to reduce racial disparities, and also to human decision makers carrying out structured decision rules.
1.5K
•Posted Content
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh,Shiori Sagawa,Henrik Marklund,Sang Michael Xie,Marvin Zhang,Akshay Balsubramani,Weihua Hu,Michihiro Yasunaga,Richard Lanas Phillips,Irena Gao,Tony Lee,Etienne David,Ian Stavness,Wei Guo,Berton A. Earnshaw,Imran S. Haque,Sara Beery,Jure Leskovec,Anshul Kundaje,Emma Pierson,Sergey Levine,Chelsea Finn,Percy Liang +22 more
TL;DR: WILDS is presented, a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, and is hoped to encourage the development of general-purpose methods that are anchored to real-world distribution shifts and that work well across different applications and problem settings.
1K
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis.
TL;DR: A dimensionality-reduction method is developed, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and it is shown that it improves modeling accuracy on simulated and biological data sets.
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.
TL;DR: It is shown that SIMLR is scalable and greatly enhances clustering performance while improving the visualization and interpretability of single-cell sequencing data.