World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
5825
World Ranking
13909
National Ranking
5528

Research.com Recognitions

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

Emily B. Fox is affiliated with Stanford University in the United States. Their research primarily spans the field of Computer Science, with a focus on subfields such as Artificial Intelligence, Endocrinology, Diabetes and Metabolism, Molecular Biology, Computer Vision and Pattern Recognition, and Modeling and Simulation.

Their scholarly output includes work on a variety of topics, notably:

  • Diabetes Management and Research
  • COVID-19 epidemiological studies
  • Neural Networks and Applications
  • Statistical Methods and Inference
  • Explainable Artificial Intelligence (XAI)
  • Scientific Computing and Data Management
  • Neural dynamics and brain function

Fox has published extensively in several academic venues, including:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Cytotherapy
  • Diabetes
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Their recent notable papers include:

  • "Neural Granger Causality", 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)", 2020, arXiv (Cornell University)
  • "How to build the virtual cell with artificial intelligence: Priorities and opportunities", 2024, Cell
  • "Mobility trends provide a leading indicator of changes in SARS-CoV-2 transmission", 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "It's complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US", 2021, npj Digital Medicine

The scientist frequently collaborates with colleagues such as Nicholas J. Foti, Andrew C. Miller, David Scheinker, Ramesh Johari, and David M. Maahs.

Emily B. Fox has been recognized with the Fellowship from the Alfred P. Sloan Foundation in 2015.

Best Publications

  • Stochastic Gradient Hamiltonian Monte Carlo

    Tianqi Chen;Emily Fox;Carlos Guestrin

  • A Sticky HDP-HMM With Application to Speaker Diarization

    Emily B. Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

  • A complete recipe for stochastic gradient MCMC

    Yi-An Ma;Tianqi Chen;Emily B. Fox

  • An HDP-HMM for systems with state persistence

    Emily B. Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

  • Bayesian Nonparametric Inference of Switching Dynamic Linear Models

    E Fox;E B Sudderth;M I Jordan;A S Willsky

  • Neural Granger Causality.

    Alex Tank;Ian Covert;Nicholas Foti;Ali Shojaie

  • Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

    Emily Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

  • Sparse graphs using exchangeable random measures

    François Caron;Emily B. Fox

  • Improving reproducibility in machine learning research : a report from the NeurIPS 2019 reproducibility program

    Joelle Pineau;Joelle Pineau;Philippe Vincent-Lamarre;Koustuv Sinha;Koustuv Sinha;Vincent Larivière

  • A Bayesian approach for predicting the popularity of tweets

    Tauhid Zaman;Emily B. Fox;Eric T. Bradlow

  • Sharing Features among Dynamical Systems with Beta Processes

    Emily Fox;Michael I. Jordan;Erik B. Sudderth;Alan S. Willsky

  • Bayesian nonparametric learning of complex dynamical phenomena

    Emily Beth Fox

  • JOINT MODELING OF MULTIPLE TIME SERIES VIA THE BETA PROCESS WITH APPLICATION TO MOTION CAPTURE SEGMENTATION

    Emily B. Fox;Michael C. Hughes;Erik B. Sudderth;Michael I. Jordan

  • Bayesian Nonparametric Methods for Learning Markov Switching Processes

    E B Fox;E B Sudderth;M I Jordan;A S Willsky

  • Learning the Parameters of Determinantal Point Process Kernels

    Raja Hafiz Affandi;Emily Fox;Ryan Adams;Ben Taskar

  • Neural Granger Causality for Nonlinear Time Series

    Alex Tank;Ian Covert;Nicholas Foti;Ali Shojaie

  • Bayesian nonparametric covariance regression

    Emily B. Fox;David B. Dunson

  • The Sticky HDP-HMM: Bayesian Nonparametric Hidden Markov Models with Persistent States

    Emily B. Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

  • Control variates for stochastic gradient MCMC

    Jack Baker;Paul Fearnhead;Emily B. Fox;Christopher Nemeth

  • Expectation-Maximization for Learning Determinantal Point Processes

    Jennifer A Gillenwater;Alex Kulesza;Emily Fox;Ben Taskar

Frequent Co-Authors

Erik B. Sudderth
Erik B. Sudderth University of California, Irvine
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Ben Taskar
Ben Taskar University of Washington
Andrew Miller
Andrew Miller University of Illinois at Urbana-Champaign
Paul Fearnhead
Paul Fearnhead Lancaster University
David B. Dunson
David B. Dunson Duke University
Brian Litt
Brian Litt University of Pennsylvania
Carlos Guestrin
Carlos Guestrin Stanford University
Joelle Pineau
Joelle Pineau McGill University

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