Saarland University, Machine Learning Group, Fak. MI - Mathematik und Informatik, Campus E1 1, 66123 Saarbrücken, Germany     

Machine Learning Group
Department of Mathematics and Computer Science - Saarland University

MARTIN SLAWSKI

Researcher/Ph.D. Student,
Faculty of Mathematics and Computer Science,
Saarland University

Address:
Building E 1 1, Room 224
Universität des Saarlandes
Postfach 15 11 50
D - 66041 Saarbrücken
Germany

phone: +49 (0) 681 302-57333

 

ABOUT ME

I graduated in Statistics at Ludwig-Maximilians-Universitaet Muenchen before joining the Machine Learning Group at Saarland University. My present research focus is sparse recovery and compressed sensing for positive signals, driven by a cooperation with the local Computational Proteomics Group. During this project, we have developed approaches for the automatic deconvolution of isotopic patterns in mass spectrometry. The key ingredients of the ongoing theoretical analysis are elements of convex geometry and random matrix theory.

PROJECTS

Deconvolution of isotopic patterns in computational Proteomics

(in collaboration with the Center of Bioinformatics, Junior Research Group for Computational Proteomics and Protein-Protein Interactions)

In high-resolution protein mass spectrometry, one records signals which are composed of peak patterns, each representing a particular peptide. Identifying these patterns from noisy data is highly non-trivial due to the presence of overlaps (as displayed in the figure above), unknown charge states and irregular deviations from biochemical models.

Sparse Recovery for positive signals

Undetermined linear systems of the form y = Ax in conjunction with sparsity assumptions on x have well been studied in the last years. We work on the special case where A and x are constrained to have nonnegative components only.

 

 

PUBLICATIONS

The structured elastic net for quantile regression and support vector classification, Statistics and Computing, to appear

Revised version 

R Code 

Feature Selection Guided by Structural Information, The Annals of Applied Statistics 4(2), 1056-1080
(with Wolfgang zu Castell and Gerhard Tutz)

Journal version 

Technical Report 51, Department of Statistics, University of Munich 

Stability and Aggregation of ranked gene lists, Briefings in Bioinformatics, 10, 556-568
(with Anne-Laure Boulesteix)

Technical Report 59, Department of Statistics, University of Munich 

Gene Selector package

CMA: a comprehensive Bioconductor package for supervised classification with high-dimensional data, BMC Bioinformatics 9:439
(with Martin Daumer and Anne-Laure Boulesteix)

Technical Report 29, Department of Statistics, University of Munich 

CMA package

Windows .zip file