Projects | Publications | CV

Bioinformatics - Computational Metagenomics - Metabolomics
Machine Learning - Network Science - Nonlinear PCA - ICA

	Since 2016 Research and Innovation Centre FEM - Fondazione Edmund Mach San Michele all’Adige (TN), Italy	Senior Research Scientist in Bioinformatics Conservation Genomics Food and Nutrition, Gut Microbiome and Human Health Strain-level Metagenomics Wolbachia bacteria evolution
	2013 - 2016 Laboratory of Computational Metagenomics (Nicola Segata) Centre for Integrative Biology (CIBIO) University of Trento, Italy	Postdoctoral research Metagenomics analyses of microbial communities Strain-level Metagenomics project PanPhlAn, published in Nature Methods (2016)
	2010 - 2013 Computational Biology IASMA Research and Innovation Centre Fondazione Edmund Mach S. Michele all'Adige (TN), Italy	Postdoctoral research Metabolomics data analysis Gene expression analysis Network science
	2006 - 2010 Bioinformatics/Toponomics Group Competence Center - Functional Genomics CC-FG Ernst-Moritz-Arndt-University of Greifswald	Postdoctoral research Expression time course analysis of the intraerythrocytic developmental cycle of the malaria parasite Plasmodium falciparum. Nonlinear PCA toolbox for MATLAB Lecture 2009 "Komplexe Netzwerke", see also: Network-Science.org - Complex Networks and Systems
	2002 - 2006 Bioinformatics Group Max Planck Institute of Molecular Plant Physiology, Potsdam	PhD project. High-dimensional data from metabolomics, proteomics, and transcriptomics were analysed with respect to visualization and feature extraction. [ PhD thesis \| ICA \| Nonlinear PCA (NLPCA) ]
	1998 - 2002 Intelligent Data Analysis Group (Klaus-Robert Müller) Fraunhofer Institute FIRST, Berlin	Computer science diploma project. Research on neural networks and machine learning tools with focus on developing a hierarchical nonlinear PCA. [ Nonlinear PCA (NLPCA) ]
	2000 CIDA Centro de Investigaciones de Astronomia, Venezuela	Internship: Astronomical data analysis with neural networks. Spectral data of stars used to predict and classify the absolute magnitude and other fundamental parameters of stars. [ Stellar spectral classification ]
	2002 Humboldt University Berlin, Germany	Degree in Computer Science (German Diplom)

Matthias Scholz, PhD

Research Scientist