Department of Mathematics and Computer Science

A DFG-funded research initiative of the Berlin universities (FU, HU, and TU Berlin) and the Weierstrass institute with the University of Oxford. Our vision and mission is to train a new generation of researchers in this vibrant field of stochastics that presents formidable theoretical challenges in its mathematical foundations and that breaths and grows through its applications. The fields of applications of particular interest to the 25 researchers from Berlin and Oxford are Mathematical Finance, Natural Sciences und Statistics and Machine Learning.

The Berlin Mathematical School (BMS) is a joint graduate school of the mathematics departments of the three major Berlin universities, TU Berlin, FU Berlin, and HU Berlin.
BMS offers a uniform setting for graduate studies (from bachelor to PhD) in mathematics in Berlin, a coordinated, highly diverse course program in English (Phase I, from bachelor to qualifying exams in two years, German language skills are not necessary), access to all math research groups in Berlin for a PhD (Phase II), in particular to the DFG Research Training Groups and other ongoing research projects, intensive mentoring during the whole duration of studies, and support in non mathematical issues ranging from housing to visas and from child care to language courses.
BMS invites excellent mathematics students from Germany, Europe and all over the world to join BMS - and to make good use of the ample opportunities offered by the rich and diverse mathematics teaching and research environment in Berlin.

MATH+, the Berlin Mathematics Research Center, is a cross-institutional and interdisciplinary Cluster of Excellence. It sets out to explore and further develop new approaches in application-oriented mathematics. Emphasis is placed on mathematical principles for using ever larger amounts of data in life and material sciences, in energy and network research, and in the humanities and social sciences.

Complex processes involving cascades of scales are ubiquitous in current natural science research. Such processes feature more than two characteristic scales, their smallest and largest scales are widely separated, and much of their scale range participates in the process interactions. Also, they are often too complex for experimental studies, but with the steady increase of compute power there is hope that they can be understood through computational simulations. Such simulations remain very challenging, however, as their wide range of scales is associated with very large numbers of degrees of freedom and in many cases this will prohibit brute-force all-detail computational modeling far into the future...

MODAL is a cluster of Mathematical Optimization and Data Analysis Laboratories
On the campus of Freie Universität Berlin. It brings together partners from research on one hand - Freie Universität Berlin, Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB),the Berlin Centre for computer science Konrad-Zuse-Zentrum – and economy and industry (free enterprise) on the other hand. Its fields of research and application are data driven mathematical modeling, simulation and optimization of complex processes in logistics, Clinical Research and Health Care. MODAL’s goal is also the optimization of networks like those in public transport, gas supply and medical data. Mathematics is a key factor in making these developments successful in the future. MODAL works on application-oriented, interdisciplinary and collaborative research solutions.

Rough analysis constitutes a nonlinear extension of distribution theory. It is crucial for understanding singular stochastic dynamics, possible renormalizations, and nonlinear effects of signals. Recent developments have led to profound mathematical structures with significant geometric and algebraic aspects. Our aim is to sustainably foster the mutual relationships with rough analysis through an intensive interplay of analysis, algebra/geometry, and probability theory, complemented by applications in statistics, robust modeling under uncertainty, stochastic control theory, and mathematical finance.