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.

People

Sumit Basu 

Interactive machine learning, interactive tutoring systems, educational data mining, dynamic languages for scientific computation, machine learning for creativity applications, machine learning for systems applications, auditory analysis and synthesis

 

Misha Bilenko 

Learning from user behavior, predictive personalization, scaling up machine learning, learning applications in online advertising, adaptive similarity functions, building tools for improving predictive accuracy

Leon Bottou

Large-scale learning, statistical machine learning, structured learning systems, stochastic gradient learning, transduction, causality and machine learning, kernel methods, neural networks, interactive machine learning, reasoning and machine learning, machine learning and semantics, compound image compression

 

Chris Burges (Manager)

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

Denis Charles

Algorithms, Complexity Theory, Graph Theory, Game Theory and Mechanism Design, Web Scale Computing, Computational Number Theory, Algebraic Number Theory and Algebraic Geometry 

 

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

 

Asela Gunawardana 

Temporal modeling and forecasting of events, modeling user intent, recommender systems, online advertising

 

Cormac Herley 

Machine learning for security applications, data analysis problems involving adversaries, fraud and abuse, risk analysis, economics and incentive problems

 

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

 

John Platt (Manager)

Improving the data/human interface, fast machine learning, automatically discovering representations

 

Erin Renshaw 

Software development, large-scale data analysis and algorithms, numerics

 

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

 

Jay Stokes 

Machine learning for security, malware classification, malicious webpage detection, active learning

 

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 

Supervised learning, algorithmic crowdsourcing (human computing), learning representations, large-scale learning, nonparametric statistics, probabilistic modeling, and their applications to web search and social media

 

Applying for Positions

  • Apply here for a Postdoctoral Researcher position for 2014 and have your application material (including references) sent to mlgapp@microsoft.com.
  • Apply here for a summer internship for 2014 and inform us that you have applied by emailing mlgapp@microsoft.com. We only consider Ph.D. students. (closed)
Recent Publications

    2014

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    2012

    2011