Martin Genzel © privat BMS Phase II student Martin Genzel was appointed a GAMM Junior by the Gesellschaft für Angewandte Mathematik und Mechanik (International Association of Applied Mathematics and Mechanics) in December 2016.

The GAMM was founded in 1922 by Ludwig Prandtl and Richard von Mises to promote scientific development in all areas of applied mathematics and mechanics. The association fosters international cooperation and currently comprises over 2000 members.

Each year, ten new GAMM Juniors are selected in recognition of their outstanding achievements in their graduate and/or doctoral theses in the field of applied mathematics or mechanics. To be eligible, these young scientists must be under the age of 32 at the time of application and have a final thesis not older than two years. The successful candidates are invited to become active in the GAMM for three years as ambassadors of young scientists in the fields of applied mathematics and mechanics, representing these disciplines within the scientific community and society. During this time, they are exempt from GAMM membership fees and are offered additional financial and moral support in their scientific endeavors. With an overlap in the term of office of the thirty members, their technical and organizational knowledge can be passed on to succeeding generations of GAMM Juniors. 

Current GAMM Juniors include BMS Phase II students Sandra Keiper and Philipp Petersen. BMS alumni Agnieszka Miedlar and Robert Altmann were GAMM Juniors until 2014 and 2016, respectively.

Martin Genzel was selected in recognition of his master's thesis entitled "Sparse Proteomics Analysis" and will be active as a GAMM Junior for three years from 2017 until the end of 2019. He is a member of the Applied Functional Analysis research group at the TU Berlin and is currently working on the project “Sparse Compressed Sensing based Classifiers for -omics mass-data” under the supervision of Prof. Dr. Gitta Kutyniok. In his research, Martin particularly focuses on high-dimensional data analysis and compressed sensing, as well as several topics from machine learning.

Congratulations Martin!