Prof. Dr. Emmanuel Müller

Sparse2Big: Imputation and Fusion for Massive Sparse Data

(2017 - 2020) Helmholtz Association [total funding of 2.2 Mio. €]
joint project with HMGU, DZNE, DKFZ, FZJ, GFZ/HPI, HZI, MDC, UFZ

Large data sets with many variables frequently contain unobserved, missing or noisy entries. Dealing with these missing values is crucial for any later step of data analytics. Only when properly dealing with these sparse data sets, including the combination of multiple sparse observations of the same entity from different views, we can hope to achieve meaningful big data analytics results. Hence we develop, evaluate and share methods for (1) data imputation in order to fill these missing values with statistical methods and (2) big data analytics making these methods aware of missing values. Our technology will be an enabler for many research areas ranging from patient data in medicine to remote sensing in geography.

OSIMAB: Online Security Management System for Bridges

(2017 - 2020) Federal Ministry of Transportation and Digital Infrastructure [total funding of 2 Mio. €]
joint project with Bundesanstalt für Straßenwesen (BASt), ITC Engineering GmbH & Co. KG, Hottinger Baldwin Messtechnik GmbH, TU Berlin

In the project "Online Security Management System for Bridges" we develop anomaly detection algorithms that can detect abnormal situations in high frequent sensor streams. With such a detection we enhance risk analysis and enable predictive analytics for future bridge monitoring.

Geo.X Young Academy: "Geo Data Science"

(2016 - 2019) Helmholtz Association [total funding of 3 Mio. €]
joint project with TU Berlin, HU Berlin, FU Berlin, Univ. Potsdam, Alfred Wegener Institute, German Aerospace Center, Museum of Natural History, German Research Centre for Geosciences, Institute for Advanced Sustainability Studies Potsdam

Our goal is to train a new generation of outstanding young scientists based on a strong collaboration systematically linking methodological expertise from computer science and mathematics with earth sciences. With this approach we aim to establish a new quality in interdisciplinary collaborations in the field of "Geo Data Science"