Miguel A. Carreira-Perpinan, University of California, Merced, USA. Optimization (in particular for deep learning and decision trees), dimensionality reduction, mean-shift algorithms, unsupervised learning.
Alexander Clark, King's College London, UK. Grammatical inference, unsupervised learning in NLP, natural language learning, mathematical linguistics.
Corinna Cortes, Google Research, USA. Kernel methods, boosting, feature selection.
Russ Greiner, University of Alberta, Canada. Medical informatics, active/budgeted Learning
Arthur Gretton, University College London, UK. Hypothesis testing, kernel methods
Maya Gupta, Google Research, USA. Interpretable machine learning, clustering, regression, multi-task learning, constrained optimization, stochastic gradient descent, large-scale learning
Isabelle Guyon, ClopiNet, USA. Feature selection, causaltity, model selection, automatic machine learning, computer vision, kernel method
Zaid Harchaoui, University of Washington. Convex optimization, high-dimensional statistics, kernel-based methods, non-convex optimization, representation learning
Moritz Hardt, Google Research, USA. Learning theory, algorithms, optimization, privacy
Philipp Hennig, University of Tübingen. Probabilistic Numerics (including Bayesian Optimization and Quadrature); Algorithms for Bayesian Inference; Algorithms for Deep Learning (Optimization, Sampling, etc.); Gaussian Processes; Sustainable ML and ML for Sustainability.
Bert Huang, Virginia Tech, USA. structured prediction, probabilistic graphical models, relational learning, networks
Aapo Hyvärinen, University of Helsinki, Finland. Unsupervised learning, natural image statistics, neuroimaging data analysis
Tommi Jaakkola, Massachusetts Institute of Technology, USA. Approximate inference, structured prediction, deep learning
Stefanie Jegelka, Massachusetts Institute of Technology, USA. Submodularity, determinantal point processes, negative dependence, Bayesian optimization
Samuel Kaski, Aalto University, Finland. Probabilistic modelling, multiple data sources (multi-view, multi-task, multimodal, retrieval); applications in bioinformatics, user interaction, brain signal analysis
Sathiya Keerthi, Microsoft Research, USA. optimization, large margin methods, structured prediction, large scale learning, distributed training
Emtiyaz Khan, RIKEN Center for Advanced Intelligence, Japan. Variational Inference, Approximate Bayesian inference, Bayesian Deep Learning
George Konidaris, Duke University, USA. Reinforcement Learning, artificial intelligence, robotics
Andreas Krause, ETH Zurich, Switzerland. Active learning, Bayesian optimization, submodularity, sequential decision making
Sanjiv Kumar, Google Research, USA. large-scale learning, hashing, matrix factorization, nearest neighbor search, clustering
Christoph Lampert, Institute of Science and Technology, Austria. transfer learning, structured prediction, computer vision
Honglak Lee, Google and University of Michigan, Ann Arbor. Deep Learning, Deep Generative Models, Representation Learning, Reinforcement Learning, Unsupervised Learning
Daniel Lee, University of Pennsylvania, USA. Unsupervised learning, reinforcement learning, robotics, computational neuroscience
Qiang Liu, Dartmouth College, USA. Probablistic graphical models, inference and learning, computational models for crowdsourcing
Gábor Lugosi, Pompeu Fabra University, Spain. learning theory, online prediction
Chris Meek, Microsoft Research, USA. Graphical Models, recommendation systems, point processes
Qiaozhu Mei, University of Michigan, USA. Learning from text, network, and behavioral data, representation learning, interactive learning
Vahab Mirrokni, Google Research, USA. Mechanism Desgin and Internet Economics, Algorithmic Game Thoery, Distributed Optimization, Submodular Optimization, Large-scale Graph Mining
Mehryar Mohri, New York University, USA. Learning theory (all aspects, including auctioning, ensemble methods, structured prediction, time series, on-line learning, games, adaptation, learning kernels, spectral learning, ranking, low-rank approximation)
Joris Mooij, University of Amsterdam, Netherlands. Causality
Sayan Mukherjee, Duke University, USA. Bayesian methodology, computational biology, inference in dynamical systems, geometry and topology in inference, stochastic geometry and topology
Boaz Nadler, Weizmann Institute of Science, Israel. High dimensional statistics, sparsity, latent variable models, ensemble learning
Long Nguyen, University of Michigan, USA. Bayesian nonparametrics, hierarchical and graphical models, variational and geometric methods for statistical inference
Sebastian Nowozin, Google Research. Structured prediction, computer vision, deep learning
Una-May O'Reilly, Massachusetts Institute of Technology, USA. evolutionary algorithms, genetic programming,
Laurent Orseau, Google Deepmind, USA. Reinforcement Learning, Artificial General Intelligence
Jie Peng, University of California, Davis, USA. High dimensional statistical inference, graphical models, functional data analysis
Sujay Sanghavi, University of Texas, Austin, USA. Sparsity, Convex and Non-Convex Optimization, Statistical Learning, Graphical Models, Matrix Factorization
Mark Schmidt, University of British Columbia, Canada. Convex Optimization, Probabilistic Graphical Models
Marc Schoenauer, INRIA Saclay, France. Stochastic Optimization, Derivative-free Optimization, Evolutionary Algorithms, Algorithm configuration/selection
Ohad Shamir, Weizmann Institute of Science, Israel. Learning theory, optimization, theory of deep learning.
John Shawe-Taylor, University College London, UK. Statistical learning theory, kernel methods, reinforcement learning.
Xiaotong Shen, University of Minnesota, USA. Learning, Graphical models, Recommenders
David Sontag, Massachusetts Institute of Technology. Graphical models, approximate inference, structured prediction, unsupervised learning, applications to health care
Peter Spirtes, Carnegie Mellon University, USA. Bayesian networks, Causal models, Model search, Causal inference
Suvrit Sra, Massachusetts Institute of Technology, USA. Optimization, Deep Learning Theory, Non-convex Optimization, non-Euclidean geometry and optimization, Negative Dependence, Optimal Transport, Matrix Theory, Determinantal point processes
Ingo Steinwart, University of Stuttgart, Germany. Statistical learning theory, Kernel-based learning methods (support vector machines), Cluster Analysis, Loss functions
Amos Storkey, University of Edinburgh, UK. Deep Generative Models, Adversarial Learning, Probabilistic Models, Learning under Constraints, Transfer Learning, Prediction Markets
Erik Sudderth, University of California, Irvine, USA. Bayesian nonparametrics, graphical models, unsupervised learning, variational inference, Monte Carlo methods, computer vision, signal and image processing.
Csaba Szepesvari, University of Alberta, Canada. Reinforcement learning, learning theory, online learning, learning interactively