Topics – Forschungsthemen

Clustering

• Subspace Clustering
• Deep Clustering
• Spectral Clustering

Process Mining

• Conformance Checking, Outliers, Anomalies
• Rule-based Recommendations, Predictions, Conformance
• Extended events (intervals), structured events
• Resource Cost and Temporal Aspects in Process Models, Prediction
• Clustering for process mining, event identification
• Stream process mining, concept drift, online learning

Remote Sensing & Imaging

• Noise-resistant and Robust Object Detection
• Weakly/semi-/unsupervised Learning
• Superresolution Networks
• Video Instance Segmentation

Graph Machine Learning

• Representation Learning on Graphs
• Graph Neural Networks

Machine Learning with limited labeled data

• Active Learning

• Semi-Supervised Learning

(Deep) Reinforcement Learning

• Resource Allocation for Riskaware Financial Investment
• Spatial Routing and Resource Collection
• Meta Reinforcement Learning and robust Policy Learning

Machine Learning with Knowledge Basis

• Knowlege Graphs Extraction and Correction
• Multihop Reasoning using Reinforcement Learning

Semi-/Supervised Natural-Language-Processing

• Argument Mining

Modern Database Architectures for the Cloud and the Edge (Prof. Paradies)

• Database Systems on modern hardware (in particular storage)
• Database Support for Multi-Dimensional Raster Data
• Resource- and energy-efficient data processing