Research Focus
Artificial intelligence and in particular machine learning form the common content-related core of our research activities. The research institute focuses on novel intelligent and adaptive systems for business, administration and industry, but also for start-ups. By combining research in machine learning, common modeling and development methods as well as technology sets are used and further developed to support novel application possibilities and also to explore new intelligent applications.
Here, we analyze large-scale structured and unstructured datasets using clustering, classification, and text and data mining with the goal of multi-level decision making in the dynamic systems environment. We conduct research on networked intelligent transportation systems as well as on new solutions in robotics (e.g., assistance robots) and in decision support for complex problems. Furthermore, we use remote sensing data to investigate vegetation structures in forestry or disaster damage.
We work together in an interdisciplinary way and benefit from the resulting synergy effects in broad research projects.
Our competences
The research institute IAMLIS bundles machine learning methods:
- Deep Learning
- reinforcement learning
- Generative methods
- Semantic Modeling
- Text mining
- soft computing
- Fuzzy Rough Sets
- Parameter Estimation
- Signal Processing
The combination of competences results in overlapping, interdisciplinary approaches to solutions in addition to the previous research topics. In particular, we see great potential for application-oriented research in the analysis of very extensive data with the help of machine learning methods. Through the interdisciplinary combination of technical know-how and the application of methods from different perspectives, problems can be addressed holistically.
Application scenarios
Currently, we are focusing on application scenarios in several application areas that are characterized by machine learning methods:
- Ambient Assisted Living (assistance robots).
- Machine Learning assisted resource allocation and information dissemination in mobile networks.
- Matching of web content
- Pattern recognition in social media channels
- Pattern recognition in disease progression
- Forest inventories using remote sensing data
- Analysis of settlement patterns
- Glacier monitoring
- Damage detection after storm events
- Water level mapping from space
- UAV-supported geodata acquisition