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Machine Learning Department

The Machine Learning Department of Microsoft Research pushes the state of the art in machine learning. Our work spans the space from theoretical foundations of machine learning, to new machine learning systems and algorithms, to helping our partner product groups apply machine learning to large and complex tasks.

People

Sebastien Bubeck

Machine learning, combinatorial statistics, multi-armed bandits, online learning, stochastic optimization, convex optimization 

 

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

 

Applying for Positions

Recent Publications

    2015

    2014