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Machine Learning Groups

The Machine Learning Groups of Microsoft Research include a set of researchers and developers who push the state of the art in machine learning. We span the space from proving theorems about the math underlying ML, to creating new ML systems and algorithms, to helping our partner product groups apply ML to large and complex data sets.



Chris Burges (Manager)

Machine learning algorithms, optimization, machine reading, ranking for web search, dimension reduction, audio fingerprinting 


Max Chickering  

Practical applications of machine learning, learning graphical models, human computation and methods for coxswain displacement


Silviu-Petru Cucerzan 

Natural language processing, information extraction from large text collections, semantic modeling, information retrieval


Ofer Dekel 

Supervised learning algorithms, learning theory, online prediction, optimization, building large-scale machine learning systems, web search


Ran Gilad-Bachrach 

Learning theory, hypothesis testing, continuous sensing, machine learning engineering principles


Chuck Jacobs 

Dynamic language tools, machine learning, GPGPU, data visualization, adaptive document layout


Lihong Li

Reinforcement learning, multi-armed bandit, online learning, recommendation, computational advertising


Chris Meek (Manager)

Graphical models (from various perspectives: inference, learning, relational, representation, algebraic, causal), temporal models (events, sequence data), and scalable algorithms


Andrzej Pastusiak 

Machine learning algorithms, natural language modeling, HPC, GPGPU


Matthew Richardson 

Web search, online advertising, query log analysis, online privacy, community question answering, social networks, Markov logic, collective knowledge, crowdsourcing


Patrice Simard (Manager)

Interactive machine learning, active labeling, active featuring, large data sets, generalization, regularization, exploitation/exploration, neural networks


Lin Xiao 

Convex optimization, first-order and online algorithms, interior point methods, optimization software, machine learning algorithms, sparsity recovery, parallel and distributed computing


Scott Yih 

Semantic similarity and relevance, spam filtering, structured-output learning, information extraction, natural language processing, information retrieval


Dengyong Zhou 

Statistical machine learning, crowdsourcing (human computation), learning from clicks, learning representations, probabilistic modeling, game theory and mechanism design, mathematical statistics


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