• Edo Airoldi, Harvard University, USA Statistics, approximate inference, causal inference, network data analysis, computational biology.
• Pierre Alquier, ESSEC Asia-Pacific Statistical Learning theory, PAC-Bayes learning, Approximate Bayesian inference, Variational inference, High-dimensional statistics
• Animashree Anandkumar, California Institute of Technology, USA Tensor decomposition, non-convex optimization, probabilistic models, reinforcement learning.
• Bryon Aragam, University of Chicago causality, artificial intelligence, generative models, unsupervised learning, nonparametric statistics
• Krishnakumar Balasubramanian, University of California, Davis Sampling, Stochastic Optimization, Learning theory.
• Arindam Banerjee, UIUC bandits, generative models, deep learning, optimization, learning theory, federated learning
• Elias Bareinboim, Columbia University causal Inference, generalizability, fairness, reinforcement learning
• Raef Bassily, The Ohio State University differential privacy, privacy-preserving machine learning, learning theory, optimization
• Samy Bengio, Apple, USA Deep learning, representation learning
• Yoshua Bengio, University of Montreal, Canada / Mila Deep learning, learning to reason
• Quentin Berthet, Google DeepMind Convex optimization, optimal transport, differentiable programming, high-dimensional statistics
• Alexandre Bouchard, UBC mcmc, smc, phylogenetics
• Joan Bruna, NYU, USA deep learning theory, signal processing, statistics
• Miguel Carreira-Perpinan, University of California, Merced, USA Decision trees and forests, neural network compression, optimization in deep learning
• Kai-Wei Chang, UCLA Large Language Models, Trustworthy Natural Language Processing, Vision-Language Models
• Silvia Chiappa, DeepMind Causal inference, Approximate Bayesian inference, variational inference, ML fairness
• Kyle Cranmer, University of Wisconsin-Madison AI/ML for Science, Probabilistic ML, Approximate Inference, Geometric Deep Learning
• Florence d'Alche-Buc, Telecom Paris, Institut Polytechnique de Paris Kernel methods, complex output prediction, robustness, explainability, bioinformatics
• Luc De Raedt, Katholieke Universiteit Leuven, Belgium (statistical) relational learning, inductive logic programming, symbolic machine learning, probabilistic programming, learning from structured data, pattern mining
• Gal Elidan, Hebrew University, Israel
• Barbara Engelhardt, Stanford University, USA Latent factor models, computational biology, statistical inference, hierarchical models
• Kenji Fukumizu, The Institute of Statistical Mathematics, Japan Kernel methods, dimension reduction
• Christophe Giraud, Universite Paris-Saclay Clustering, network analysis, algorithmic fairness, average case computational hardness, high-dimensional statistics
• Manuel Gomez-Rodriguez, Max Planck Institute for Software Systems Fairness, interpretability, accountability, strategic behavior, human-ai collaboration, temporal point processes
• Russell Greiner, University of Alberta, Canada Medical informatics, active/budgeted Learning, survival prediction
• Quanquan Gu, UCLA optimization, theory of deep learning, reinforcement learning, LLMs, deep generative models, high-dimensional statistics
• Benjamin Guedj, Inria and University College London, France and UK Learning theory, PAC-Bayes, computational statistics, high-dimensional statistics, theory of deep learning, probabilistic models, Bayesian inference
• Rajarshi Guhaniyogi, Texas A & M University Spatial and spatio-temporal Bayesian methods for large data, Bayes theory and methods for high dimensional regressions, tensor and network-valued regressions, functional data analysis, approximate Bayesian inference, graphical models, applications in neuroimaging and environmental sciences
• Maya Gupta, University of Washington fairness, interpretability, societal issues, safety, regresssion, ensembles, shape constraints, immunology, information theory
• Aapo Hyvarinen, 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
• Prateek Jain, Microsoft Research, India Non-convex Optimization, Stochastic Optimization, Large-scale Optimization, Resource-constrained Machine Learning
• Kevin Jamieson, University of Washington Multi-armed bandits, active learning, experimental design
• Nan Jiang, University of Illinois Urbana-Champaign reinforcement learning theory
• Varun Kanade, University of Oxford learning theory; online learning; computational complexity; 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
• Mohammad Emtiyaz Khan, RIKEN Center for Advanced Intelligence, Japan Variational Inference, Approximate Bayesian inference, Bayesian Deep Learning
• Mladen Kolar, University of Southern California, USA high-dimensional statistics, graphical models
• George Konidaris, Duke University, USA Reinforcement Learning, artificial intelligence, robotics
• Aryeh Kontorovich, Ben-Gurion University metric spaces, nearest neighbors, Markov chains, statistics
• Wouter Koolen, CWI, Amsterdam Online Learning, Bandits, Pure Exploration, e-values
• Alp Kucukelbir, Columbia University & Fero Labs variational inference, statistical machine learning, approximate inference, diffusion, probabilistic programming
• Brian Kulis, Boston University Deep Learning, Clustering, Kernel Methods, Metric Learning, Self-Supervised Learning, Audio Applications, Vision Applications
• Sanjiv Kumar, Google Research representation learning, optimization, deep learning, hashing, nearest neighbor search
• Eric Laber, Duke University reinforcement learning, precision medicine, treatment regimes, causal inference
• Christoph Lampert, Institute of Science and Technology, Austria (IST Austria) transfer learning, trustworthy learning, computer vision
• Tor Lattimore, DeepMind Bandits, reinforcement learning, online learning
• Anthony Lee, University of Bristol Markov chain Monte Carlo, sequential Monte Carlo
• Honglak Lee, Google and University of Michigan, Ann Arbor Deep Learning, Deep Generative Models, Representation Learning, Reinforcement Learning, Unsupervised Learning
• Qiang Liu, The University of Texas at Austin Deep learning, neural networks, optimization, generative models, diffusion models, large language models.
• Han Liu,
• Jianfeng Lu, Duke University Monte Carlo sampling, scientific machine learning, generative models
• Gabor Lugosi, Pompeu Fabra University, Spain statistical learning theory, online prediction, concentration inequalities
• Shiqian Ma, Rice University first-order methods, stochastic algorithms, bilevel optimization, Riemannian optimization
• Michael Mahoney, University of California at Berkeley, USA randomized linear algebra; stochastic optimization; neural networks; matrix algorithms; graph algorithms; scientific machine learning
• Vikash Mansinghka, Massachusetts Institute of Technology, USA Probabilistic programming, Bayesian structure learning, large-scale sequential Monte Carlo
• Rahul Mazumder, Massachusetts Institute of Technology mathematical optimization, high-dimensional statistics, sparsity, Boosting, nonparametric statistics, shape constrained estimation, decision tree ensembles, compressing large neural networks
• Qiaozhu Mei, University of Michigan, USA Learning from text, network, and behavioral data, representation learning, interactive learning
• Vahab Mirrokni, Google Research Mechanism Desgin and Internet Economics, Algorithmic Game Theory, 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)
• Gergely Neu, reinforcement learning, learning theory, online learning, bandit theory
• Lam Nguyen, IBM Research, Thomas J. Watson Research Center Stochastic Gradient Algorithms, Non-convex Optimization, Stochastic Optimization, Convex Optimization
• Chris Oates, Newcastle University Bayesian computation, kernel methods, uncertainty quantification
• Laurent Orseau, Deepmind Reinforcement Learning, Artificial General Intelligence
• Amichai Painsky, Tel Aviv University Statistics, Information Theory, Statistical Inference, Predictive Modeling, Tree-based Models, Data Compression, Probability Estimation
• Debdeep Pati, Texas A&M University Bayes theory and methods in high dimensions; Approximate Bayesian methods; high dimensional network analysis, graphical models, hierarchical modeling of complex shapes, point pattern data modeling, real-time tracking algorithms
• Jie Peng, University of California, Davis, USA High dimensional statistical inference, graphical models, functional data analysis
• Vianney Perchet, ENSAE & Criteo AI Lab bandits, online learning, matching
• Massimiliano Pontil, Istituto Italiano di Tecnologia (Italy), University College London (UK) Multitask and transfer learning, convex optimization, kernel methods, sparsity regularization
• Alexandre Proutiere, KTH Royal Institute of Technology Reinforcement learning, statistical learning in control systems, bandits, clustering and community detection
• Maxim Raginsky, University of Illinois at Urbana-Champaign Theory of deep learning, statistical learning, optimization, applied probability, concentration of measure, dynamical systems and control
• Peter Richtarik, King Abdullah University of Science and Technology (KAUST) convex and nonconvex optimization, stochastic zero, first and second-order methods, distributed training, federated learning, communication compression, operator splitting, efficient ML
• Lorenzo Rosasco, University of Genova, Italy and Massachusetts Institute of Technology, USA Statistical learning theory, Optimization, Regularization, Inverse problems
• Ruslan Salakhutdinov, Carnegie Mellon University Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.
• Joseph Salmon, Inria High-dimensional statistics, convex optimization, crowdsourcing
• Christian Shelton, UC Riverside, USA Time series, temporal and spatial processes, point processes
• Xiaotong Shen, University of Minnesota, USA Learning, Graphical models, Recommenders
• Ilya Shpitser, Johns Hopkins University causal inference, missing data, algorithmic fairness, semi-parametric statistics
• Mahdi Soltanolkotabi,
• Bharath Sriperumbudur, Pennsylvania State University Kernel Methods, Regularization, Theory of Functions and Spaces, Statistical Learning Theory, Nonparametric Estimation and Testing, Functional Data Analysis, Topological Data Analysis
• Ingo Steinwart, University of Stuttgart, Germany Statistical learning theory, Kernel-based learning methods (support vector machines), Cluster Analysis, Loss functions
• Sebastian Stich, CISPA Helmholtz Center for Information Security convex and nonconvex optimization, stochastic optimization, large-scale and distributed training, federated learning
• Weijie Su, University of Pennsylvania Differential privacy, deep learning theory, LLMs, high-dimensional statistics, optimization
• Qiang Sun, University of Toronto Discrete diffusion models, ensemble learning, generalization, trustworthy machine learning
• Csaba Szepesvari, University of Alberta, Canada reinforcement learning, sequential decision making, learning theory
• Jian Tang, Mila-Quebec AI Institute deep generative models, graph neural networks, geometric deep learning, protein design, drug discovery
• Ambuj Tewari, University of Michigan, USA learning theory, online learning, bandit problems, reinforcement learning, optimization, high-dimensional statistics
• Matthew Thorpe, University of Warwick optimal transport, semi-supervised learning, unsupervised learning, graph-based learning
• Jin Tian, Mohamed bin Zayed University of Artificial Intelligence causal inference, Bayesian networks, probabilistic graphical models
• Silvia Villa, Genova University, Italy optimization, convex optimization, first order methods, regularization
• Zhaoran Wang, reinforcement learning
• Kilian Weinberger, Cornell University, USA Deep Learning, Representation Learning, Ranking, Computer Vision
• Martha White, University of Alberta reinforcement learning, representation learning
• Lin Xiao, Meta FAIR convex optimization, stochastic optimization, optimization for machine learning, reinforcement learning, parallel and distributed optimization
• Kun Zhang, causality, transfer learning, kernel methods, unsupervised deep learning
• Zhihua Zhang, Peking University, China Bayesian Analysis and Computations, Numerical Algebra and Optimization
• Zhengyuan Zhou, contexutal bandits, online learning, game theory
• Mingyuan Zhou, The University of Texas at Austin Approximate inference, Bayesian methods, deep generative models, discrete data analysis
• Ji Zhu, University of Michigan, Ann Arbor Network data analysis, latent variable models, graphical models, high-dimensional data, health analytics.