IRJS Editorial Board
International Research Journal of Series
Editors-in-Chief
Michael Chen, Stanford University
Sarah Johnson, Massachusetts Institute of Technology
Managing Editors
David Martinez, University of California, Berkeley
Jennifer Park, Microsoft Research
Robert Kim, Cornell University
Lisa Thompson, University of Washington
Editorial Assistant
Amanda Wilson, Columbia University
Production Editor
Kevin Rodriguez, University of Chicago and Northwestern University
Web Master
Daniel Lee
IRJS Action Editors
James Anderson, Princeton University, USA
Statistical inference, Bayesian methods, computational statistics
Maria Rodriguez, ESSEC Business School
Statistical learning, variational inference, high-dimensional statistics
Thomas Williams, California Institute of Technology, USA
Tensor methods, optimization, probabilistic modeling
Christopher Brown, University of Chicago
Graphical models, causality, latent variable models
Patricia Davis, University of California, Los Angeles
Sampling methods, stochastic optimization, learning theory
Richard Miller, University of Illinois Urbana-Champaign
Bandit algorithms, deep learning, federated systems
Nancy Garcia, Columbia University
Causal inference, fairness, reinforcement learning
Daniel White, Ohio State University
Privacy-preserving ML, learning theory, optimization
Susan Clark, Apple Research, USA
Deep architectures, representation learning
Mark Thompson, University of Montreal, Canada
Deep learning, reasoning systems
Paul Walker, Google DeepMind
Convex optimization, transport theory, high-dimensional statistics
Jessica Adams, University of British Columbia
MCMC methods, sequential Monte Carlo, computational biology
Brian Scott, New York University, USA
Deep learning theory, signal analysis, statistical methods
Matthew Harris, University of California, San Diego
Decision trees, neural compression, optimization
Karen Lewis, University of California, Los Angeles
Language models, trustworthy NLP, multimodal learning
Steven Young, DeepMind Research
Causal inference, Bayesian methods, fairness in ML
Donald King, University of Wisconsin-Madison
Scientific ML, probabilistic methods, geometric learning
Barbara Moore, Apple Research
Optimal transport, geometric approaches
George Hill, Telecom Paris, France
Kernel methods, complex prediction, bioinformatics
Edward Baker, KU Leuven, Belgium
Relational learning, symbolic ML, probabilistic programming
Rachel Green, University of Cambridge, UK
Time series analysis, forecasting, econometrics
Andrew Mitchell, University of Toronto, Canada
Bayesian nonparametrics, Gaussian processes
Sophia Roberts, ETH Zurich, Switzerland
High-dimensional inference, sparsity, statistical theory
Jonathan Harris, University of Oxford, UK
Network analysis, graphical models, community detection
IRJS-MLOSS Editors
Alexandre Martin, Meta AI Research
Supervised learning, convex optimization, sparse methods, ML software
Emma Wilson, Eindhoven University, Netherlands
Automated ML, meta-learning, machine learning systems
Peter Chen, Hong Kong University of Science and Technology
ML systems, automated ML, kernel methods, decision trees
Charles Evans, Telecom ParisTech & University of Edinburgh
AI systems, big data analytics, streaming data
IRJS Advisory Board
Kenji Tanaka, RIKEN Institute, Japan
William Harris, University of Massachusetts Amherst, USA
Margaret Scott, Oregon State University, USA
Richard Davis, Stanford University, USA
James Wilson, Brown University, USA
Barbara Green, University of Toronto, Canada
Robert Taylor, University of California Berkeley, USA
Nancy Adams, Massachusetts Institute of Technology, USA
Paul Mitchell, University of Pennsylvania, USA
Donald White, InferLink Corporation, USA
Lisa Martin, Carnegie Mellon University, USA
Christopher Lee, Imperial College London, UK
Steven Brown, Google Research, USA
Michael Johnson, Massachusetts Institute of Technology, USA
Thomas Davis, Rulequest Research, Australia
Jennifer Harris, University of California Berkeley, USA