M.J.T. Reinders M.J.T. Reinders


Educational activities

- Coordinator of “Master track Bioinformatics” within the Computer Science program
- Supervised ~100 master students
- Lecturer of 7 graduate courses (currently 1), “Functional Genomics and Systems Biology”
- Lecturer of 10 undergraduate courses (currently 3), ao “Datamining” and “Bioinformatics” 
- Lecturer of 7 postgraduate courses (currently 1), “NBIC course on Pattern Recognition”

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Prof Reinders holds a position at the Delft University of Technology as well as at the Leiden University Medical Center. In Delft he heads the Pattern Recognition and Bioinformatics section in one of the Computer Science departments of the Faculty of Electtrical Engineering, Mathematics and Computer Science. The section consists of three research labs: Delft Bioinformatics Lab (5 staff); Pattern Recognition Lab (2 staff); Computer Vision Lab (3 staff) and includes a group of ~60 people (incl. ~10 scientific staff members (HL/UHD/UD’s), ~30 PHDs/Postdocs, ~20MSCs).  In Leiden he heads the Leiden Computational Biology Center.  This center generates new biological insights with clinical applicabilities using computational tools to analyse molecular data. The groupconsists of ~15 people (incl. 3 scientific staff memberts (tenure trackers), ~12PDs/MScs).

Professional memberships

From 2017       Member scientific advisory board Informatics Platform Netherlands
From 2014       Member scientific advisory board Dutch Techcentre for Life Science
From 2013       Visiting professor at Clinical Genetics, Free University Medical Centre
From 2011       Director of the TUD-EEMCS Graduate School
2010-2017       Scientific Director of the Netherlands Bioinformatics Center (NBIC)
From 2009       Heading the Pattern Recognition and Bioinformatics Section (PRB), Computer 2005-2015       Chair of the TUD-EEMCS Scientific Advisory Board (VCWB)

Research description

Prof Reinders group in Delft is recognized as international experts on machine learning and in his bioinformatics research he applies the cutting-edge machine learning expertise to develop data-driven analysis methodologies to gain molecular biology insights. His group in Leiden uses computational biology to progress clinical applicability of molecular data with a focus on newest technologies, such as single cell or spatio-temporal omics data. He initiated work on molecular classification and genetic network modelling, and has a strong track record on next-generation sequencing analysis, network-based analysis, as well as integration of genomic data.

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