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Welcome to Statistics and Machine Learning

The Division of Statistics and Machine Learning has a complete programme of undergraduate and graduate education and research. The bachelor's and master's programmes are focused on data analysis and machine learning, respectively, and provide new and interesting career options. Much of the research is conducted as collaborative efforts with end-users and organizations outside the academic world.

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Two WASP PhD students to STIMA

Hector Rodriguez-Deniz is new WASP PhD student and Caroline Svahn is new industrial PhD student at Ericsson.

Jose Pena promoted

Jose M Pena, was recently promoted to associate professor in machine learning. Jose is doing research in machine learning, specifically probabilistic graphical models and causality.

Anders Eklund in the limelight

Anders Eklund at the division has recently published an article in PNAS which has recieved intense media coverage in for example NY Times, Wired, The Economist and Nature. Anders' work showed that group analysis from fMRI data can be seriously flawed with dramatically inflated false positive rates.

We are hiring lecturers

50% research time in at least five years. See the full announement here.

New Post doc

Matias Quiroz is a new post doc at the division. Matias recently defended his PhD thesis in Statistics at Stockholm University. Matias specializes in Bayesian inference for big data problems.

New PhD student in Statistics

Sarah Alsaadi is new PhD student in Statistics at the division. Sarah is supervised by Linda Wänström, Robert Thornberg (IBL) and Mattias Villani.


Statistik och dataanalysprogrammet
Master's Programme in Statistics and Machine Learning

New publications

Polya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
IEEE Transactions on Pattern Analysis and Machine Intelligence

Efficient Covariance Approximations for Large Sparse Precision Matrices
Journal of Computational and Graphical Statistics

Speeding Up MCMC by Efficient Data Subsampling
Journal of the American Statistical Association

Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates
Human Brain Mapping

Tree Ensembles with Rule Structured Horseshoe Regularization
Annals of Applied Statistics

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Page responsible: Mattias Villani
Last updated: 2018-10-12