- Alan Fern (Reinforcement Learning, Inductive Logic Programming and Relational Learning, Planning and Control, Statistical Relational Learning, Structured Output Prediction)
- Alex Smola (Large-Margin Methods, Kernel Methods, Web Mining)
- Amos Storkey (Gaussian Processes, Bayesian Inference, Cognitive Models of Learning, Time-Series Analysis)
- Ashutosh Saxena (Robotics, Planning and Control, Vision)
- Corinna Cortes (Large-Margin Methods, Kernel Methods, Probabilistic Models, Ranking and Preference Learning)
- Csaba Szepeszvari (Reinforcement Learning, Learning Theory, Monte Carlo Methods, Online Learning, Planning and Control)
- Dan Roth (Natural Language Processing, Structured Output Prediction, Text Mining)
- David Blei (Latent-Variable Models and Topic Models, Bayesian Inference, Monte Carlo Methods, Text Mining)
- Eamonn Keogh (Time-Series Analysis, Outlier Detection)
- Eibe Frank (Empirical Insights into ML, Rule and Decision Tree Learning, Supervised Learning)
- Elena Marchiori (Evolutionary Computation, Bioinformatics, Optimization Algorithms)
- Eyke Huellermeier (Ranking and Preference Learning, Bioinformatics, Case-Based Reasoning, Fuzzy Learning Systems)
- Filip Radlinski (Information Retrieval, Ranking and Preference Learning, Web Mining)
- Francis Bach (Vision, Matrix Factorization
Methods, Sparsity and Compressed Sensing, Unsupervised Learning)
- Hendrik Blockeel (Discovery, Inductive Logic Programming and Relational Learning, Meta-Learning)
- Henrik Bostroem (Rule and Decision Tree Learning, Inductive Logic Programming and Relational Learning, Supervised Learning)
- Inderjit Dhillon (Matrix Factorization Methods, Clustering,Graph-Based Learning Methods, Unsupervised Learning)
- Joao Gama (Data Streams, Constructive Induction and Theory Revision, Online Learning)
- John Langford (Learning Theory, Active Learning)
- Jure Leskovec (Social Network Analysis, Graph Mining, Web Mining)
- Kai Yu (Gaussian Processes, Bayesian Inference, Recommender Systems, Statistical Relational Learning)
- Katharina Morik (Feature Selection and Dimensionality Reduction,Text Mining, Unsupervised Learning)
- Katherine Heller (Bayesian Inference, Clustering, Hidden Markov Models, Unsupervised Learning)
- Kristian Kersting (Statistical Relational Learning, Bayesian Inference, Probabilistic Models)
- Kurt Driessens (Reinforcement Learning, Game Playing)
- Leon Bottou (Neural Networks, Deep Architectures, Large-Scale Learning, Optimization Algorithms)
- Luc De Raedt (Knowledge-Intensive Learning, Inductive Logic Programming and Relational Learning, Pattern Mining and Inductive Querying)
- Martin Zinkevich (Game Theory, Game Playing,
Multi-Agent and Co-Operative Learning, Online Learning)
- Mikhail Belkin (Manifold Learning, Graph-Based Learning Methods, Matrix Factorization Methods)
- Mikhail Bilenko (Semi-Supervised Learning, Clustering, Information Retrieval, Unsupervised Learning)
- Naftali Tishby (Clustering, Bioinformatics, Unsupervised Learning)
- Nathan Srebro (Matrix Factorization Methods, Large-Scale Learning, Online Learning, Optimization Algorithms)
- Padraig Cunningham (Case-Based Reasoning, Ensemble Methods, Recommender Systems)
- Peter Flach (Evaluation Methodology and ROC Analysis, Cost-Sensitive Learning, Inductive Logic Programming and Relational Learning, Supervised Learning)
- Phil Long (Ensemble Methods, Bioinformatics, Learning Theory, Transfer and Multi-Task Learning)
- Pierre Dupont (Grammatical Inference, Feature Selection and Dimensionality Reduction, Graph Mining, Hidden Markov Models)
- Rich Caruana (Transfer and Multi-Task Learning, Deep Architectures, Empirical Insights into ML, Evaluation Methodologyand ROC Analysis, Large-Scale Learning, Neural Networks)
- Sanjoy Dasgupta (Learning Theory, Active Learning, Clustering, Unsupervised Learning)
- Sathiya Keerthi (Optimization Algorithms, Large-Margin Methods)
- Shie Mannor (Reinforcement Learning, Learning Theory, Online Learning, Partially Observable Markov Decision Processes)
- Thomas Gaertner (Graph Mining, Kernel Methods, Structured Output Prediction, Transfer and Multi-Task Learning)
- Thomas Hofmann (Structured Output Prediction, Latent-Variable Models and Topic Models, Text Mining, Web Mining)
- Tobias Scheffer (Active Learning, Semi-Supervised Learning, TextMining, Transfer and Multi-Task Learning)
- Tong Zhang (Statistical Methods, Graph-Based Learning Methods, Optimization Algorithms)
- Volker Roth (Feature Selection and Dimensionality Reduction, Bayesian Inference, Clustering, Bioinformatics)
- Xiaojin Zhu (Cognitive Models of Learning, Latent-Variable Models and Topic Models, Natural Language Processing, Semi-Supervised Learning)
- Active Learning (John Langford, Sanjoy Dasgupta, Tobias Scheffer)
- Bayesian Inference (Amos Storkey, David Blei, Kai Yu, Katherine Heller, Kristian Kersting, Volker Roth)
- Bioinformatics (Elena Marchiori, Eyke Huellermeier, Naftali Tishby, Phil Long, Volker Roth)
- Case-Based Reasoning (Eyke Huellermeier, Padraig Cunningham)
- Clustering (Inderjit Dhillon, Katherine Heller, Mikhail Bilenko,Naftali Tishby, Sanjoy Dasgupta, Volker Roth)
- Cognitive Models of Learning (Amos Storkey, Xiaojin Zhu)
- Constructive Induction and Theory Revision (Joao Gama)
- Cost-Sensitive Learning (Peter Flach)
- Data Streams (Joao Gama)
- Deep Architectures (Leon Bottou, Rich Caruana)
- Discovery (Hendrik Blockeel)
- Empirical Insights into ML (Eibe Frank, Rich Caruana)
- Ensemble Methods (Padraig Cunningham, Phil Long)
- Evaluation Methodology and ROC Analysis (Peter Flach, Rich Caruana)
- Evolutionary Computation (Elena Marchiori)
- Feature Selection and Dimensionality Reduction (Katharina Morik, Pierre Dupont, Volker Roth)
- Fuzzy Learning Systems (Eyke Huellermeier)
- Game Playing (Kurt Driessens, Martin Zinkevich)
- Game Theory (Martin Zinkevich)
- Gaussian Processes (Amos Storkey, Kai Yu)
- Grammatical Inference (Pierre Dupont)
- Graph Mining (Jure Leskovec, Pierre Dupont, Thomas Gaertner)
- Graph-Based Learning Methods (Inderjit Dhillon, Mikhail Belkin,Tong Zhang)
- Hidden Markov Models (Katherine Heller, Pierre Dupont)
- Inductive Logic Programming and Relational Learning (Hendrik Blockeel, Henrik Bostroem, Luc De Raedt, Peter Flach, Alan Fern)
- Information Retrieval (Filip Radlinski, Mikhail Bilenko)
- Kernel Methods (Alex Smola, Corinna Cortes, Thomas Gaertner)
- Knowledge-Intensive Learning (Luc De Raedt)
- Large-Margin Methods (Alex Smola, Corinna Cortes, Sathiya Keerthi)
- Large-Scale Learning (Leon Bottou, Nathan Srebro, Rich Caruana)
- Latent-Variable Models and Topic Models (David Blei, Thomas Hofmann, Xiaojin Zhu)
- Learning Theory (John Langford, Phil Long, Sanjoy Dasgupta, Shie Mannor, Csaba Szepeszvari)
- Manifold Learning (Mikhail Belkin)
- Matrix Factorization Methods (Francis Bach, Inderjit Dhillon, Mikhail Belkin, Nathan Srebro)
- Meta-Learning (Hendrik Blockeel)
- Monte Carlo Methods (David Blei, Csaba Szepeszvari)
- Multi-Agent and Co-Operative Learning (Martin Zinkevich)
- Natural Language Processing (Dan Roth, Xiaojin Zhu)
- Neural Networks (Leon Bottou, Rich Caruana)
- Online Learning (Joao Gama, Martin Zinkevich, Nathan Srebro, Shie Mannor, Csaba Szepeszvari)
- Optimization Algorithms (Elena Marchiori, Leon Bottou, Nathan Srebro, Sathiya Keerthi, Tong Zhang)
- Outlier Detection (Eamonn Keogh)
- Partially Observable Markov Decision Processes (Shie Mannor)
- Pattern Mining and Inductive Querying (Luc De Raedt)
- Planning and Control (Ashutosh Saxena, Csaba Szepeszvari, Alan Fern)
- Probabilistic Models (Corinna Cortes, Kristian Kersting)
- Ranking and Preference Learning (Corinna Cortes, Eyke Huellermeier, Filip Radlinski)
- Recommender Systems (Kai Yu, Padraig Cunningham)
- Reinforcement Learning (Kurt Driessens, Shie Mannor, Csaba Szepeszvari, Alan Fern)
- Robotics (Ashutosh Saxena)
- Rule and Decision Tree Learning (Eibe Frank, Henrik Bostroem)
- Semi-Supervised Learning (Mikhail Bilenko, Tobias Scheffer, Xiaojin Zhu)
- Social Network Analysis (Jure Leskovec)
- Sparsity and Compressed Sensing (Francis Bach)
- Statistical Methods (Tong Zhang)
- Statistical Relational Learning (Kai Yu, Kristian Kersting, Alan Fern)
- Structured Output Prediction (Dan Roth, Thomas Hofmann, Thomas Gaertner, Alan Fern)
- Supervised Learning (Eibe Frank, Henrik Bostroem, Peter Flach)
- Text Mining (Dan Roth, David Blei, Katharina Morik, Thomas Hofmann, Tobias Scheffer)
- Time-Series Analysis (Amos Storkey, Eamonn Keogh)
- Transfer and Multi-Task Learning (Phil Long, Rich Caruana,Thomas Gaertner, Tobias Scheffer)
- Unsupervised Learning (Francis Bach, Inderjit Dhillon, Katharina Morik, Katherine Heller, Mikhail Bilenko, Naftali Tishby, Sanjoy Dasgupta)
- Vision (Ashutosh Saxena, Francis Bach)
- Web Mining (Alex Smola, Filip Radlinski, Jure Leskovec, Thomas Hofmann)
- Wolfram Burgard (Robotics)
- Pedro Felzenszwalb (Vision)
- Lillian Lee (NLP)
- Christina Leslie (Computational Biology)
Jonathan Schaeffer (Computer Games)
- Yoav Tock (Computer Systems and Networking)
Gerhard Widmer (Computer Music & Arts)
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