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Head
Univ.-Prof. Dr. rer. nat. Dr. h. c. mult. A. K. Louis
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Tel.: +49 (0)681/302-3529
Room: 4.10
Office
Claudia Stoffer
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Tel.: +49 (0)681/302-3018, Fax: 4435
Room 4.15
Former Research Associates
Mitarbeiter bis 2019
Prof. Dr. Christian Schorr
Priv.-Doz. Dr. Gaël Rigaud
Mitarbeiter bis 2018
Priv.-Doz. Dr. Aref Lakhal
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Mitarbeiter bis 2016
Univ.-Prof. Dr. Bernadette Hahn-Rigaud
Mitarbeiterin bis 2016
Dipl.-Math. Yvonne Johann
Mitarbeiterin bis 2015
Prof. Dr. Mohammad Alakel Abazid
Associate Professor at KFU (Saudi Arabia)
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Mitarbeiter bis 09/2014
Dr. Martin Riplinger
Mitarbeiter bis 03/2014
Dr. Holger Kohr
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Mitarbeiter bis 12/2013
Dr. Andreas Groh
Mitarbeiter bis 2012
Dipl.-Math. Sabrina Bechtel
Mitarbeiterin bis 2012
Dr. Jochen Krebs
Mitarbeiter bis 2011
Dr. David Theis
Dr. Thomas Weber
Mitarbeiter bis 2009
Dr. Erik Wallacher
Mitarbeiter bis März 2008
Dr. Uwe Schmitt
Mitarbeiter bis 2008
Dr. Werner Karpen
Mitarbeiter bis Oktober 2007
Dr. Roman Müller
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Mitarbeiter bis Dezember 2007
Dr. Frank Schöpfer
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Mitarbeiter bis März 2007
Univ.-Prof. Dr. Thomas Schuster
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Homepage
Dr. Pierre Lobel
Dr. Hellai Abdullah
Dr. Martina Bloß-Rieder
Dr. Gunter Dertnig
Dr. Rainer Dietz
Dr. Peter Dörr
Dr. Bertolt Eicke
Volker Gebhard
Dr. Norbert Gorenflo
Dr. Markus Jäckels
Prof. Dr. Peter Jonas
Prof. Dr. Jürgen Kremer
Prof. Dr. Martin Böhm
Prof. Dr. Robert Plato
Univ.-Prof. Dr. Andreas Rieder
Univ.-Prof. Dr. Klaus-Dietz Tönnies
Univ.-Prof. Dr. Dr. h.c. Peter Maaß
Mathematics in Biology and Medicine
In cooperation with external specialists we develop models for cell migration and collagen matrix modification for better understanding human biology and improvement of future therapy strategies.
Signal Processing and Pattern Recognition
Raw signals practically never exhibit the searched-for information. Hence methods for noise rejection, data reduction and extraction of relevant information are investigated.
Inverse Problems
Inverse problems arise when a searched-for quantity 
is not directly observable. A representative example is computerized tomography. The approximate inverse, developed in the institute, is a versatile method that leads to highly efficient algorithms. Even features can directly be calculated from the data without the detour over the density itself. The images on the left are reconstructions from real data and show the superiority of the approach. The field of application includes nondestructive testing, Maxwell's equation and biology/medicine. For more details see Inverse Problems.