Prof. Dr. Alan Akbik conducts research in the field of machine learning (ML) and natural language processing (NLP). His goal is to enable machines to capture, understand, and use natural language like a human.
To this end, he has developed one of the world's leading deep learning frameworks for NLP, which is already being used in over 1,000 research and industrial projects.
- Deep Learning
- Natural Language Processing (NLP)
- Machine Learning (ML)
- 3 Patents at IBM Research
- Zalando Outstanding Achievement Award
Professor Jäschke and his team develop and optimize methods in the areas of Big Data and Machine Learning, especially in the aplication fields of Natural Language Processing, Social Bookmarking and Recommendation Systems. This includes the Collection (e.g. through focussed Crawling), Compilation, Annotation (e.g. by means of Crowdsourcing) and Curation of suitable records (data sets).
Further, it includes the Adaptation and Improvement of appropriate Algorythms (e.g. Named Entity Recognition, Classification, Clustering, Information Extraction, etc.) culminating in the development of web-based analysis platforms.
- GPU calculator
- diverse datasets
Mendling's research focuses on the question how business processes can be efficiently and effectively supported by information systems. To this end, Mendling and his team address questions of the information technology as well as of managerial questions.
Within the framework of business process management they developed a series of technical solutions for process mining and also management tools like the BPM Billboard.
The expertise of Mendling and his team is summarized in the seminal textbook "Fundamentals of Business Process Management", which is used by over 250 universities in 70 countries.
- cooperation with businesses from different branches
- design of training concepts for process management
- performance of training courses and projects for process improvement
- development of new analysis software for business processes
In the context of his projects, Mendling worked together with many medium-sized companies and DAX-companies from different branches.
LL.M. Digitalization and Tax Law
alta4 Geoinformatics AG
Prof. Hostert’s explores cutting-edge satellite data analysis. His main focus lies on questions regarding the global change, particularly large-scale mapping in agrarian- and forestry systems and near-nature ecological systems worldwide. He analyses the change of the earth’s surface through different methods, for example with machine learning, big data, time series analyses, hyperspectral and multisensor approaches, as well as multiscale analyses. Regional expertise of the team covers Germany, the mediterranean areas and South America, as well as Central Asia.
- satellite data analysis
- AI in remote sensing
- large-scale remote sensing analysis with big data approaches (particularly Sentinel-2, Landsat), funded through projects of the BMWi, BMBF, BMEL, as well as the EU
- scientific monitoring of satellite missions (Landsat Science Team, EnMAP ScientificAdvisory Group)
- satellite based mapping and land use analysis for NGOs, authorities and global tech- and logistics companies
Prof. Haerdle’s main research interests are quantitative finance, esp. multivariate methods in banking and finance, dimension reduction techniques, and computational statistics. In his roles both as coordinator of the Collaborative Research Center “Economic Risk” (CRC 649) and director of the interdisciplinary Center for Applied Statistics and Economics (C.A.S.E.) he primarily investigates economic risks on a global scale. Prof. Haerdle’s research aims at facilitating the evaluation of such risks and to reduce uncertainty to improve economic actors’ decision-making.
Prof. Haerdle is Distinguished Visiting Professor Wang Yanan Institute for Studies in Economics (WISE) at Xiamen University, China, as well as director of the International Research Training Group “High Dimensional Non Stationary Time Series” (ITRG 1792). Among other distinctions he received the “Econometric Theory Multa Scripsit Award” in 2012.
- multivariate statistical analysis (factor analysis, cluster Analysis, etc.)
- portfolio optimisation
- risk management
- pricing derivatives
- functional data analysis
- non- and semi-parametric methods
- data visualisation
- Ongoing cooperation with and lecturing for leading international financial institutions
- Center for Applied Statistics and Economics (C.A.S.E.): interdisciplinary research centre with the goal to analyze and solve current complex economic problems and those arising in related fields with the help of quantitative methods and computing. Its research subjects range from weather risk, aging societies, crime to property markets
- Collaborative Research Center “Economic Risk” (CRC 649): center of transdisciplinary research where insights from economics, mathematics and statistics converge to analyze economic risks and risk factors. The CRC offers an international platform for discussion of the latest research results and collaborations