Knowledge Technology Research Group

The Knowledge Technology Research Group is the youngest part of the human-computer interface research at the Faculty of Information Technology. Despite that, it has a long track record of research and development results in a wide range of areas related to knowledge extraction, representation, and management, as well as advanced machine learning and big data processing. The team has participated in many international and national projects funded by various grant agencies or relevant industries.

Research interests

  • information extraction, knowledge representation, question answering, context-based processing, social media analysis, opinion mining, information retrieval from multimedia, emotion analysis, human-machine interaction, cognitive ergonomics
  • embedded intelligence, down-sizing deep learning models for low-power chips, mobile phone-adaptation of training procedures, big data processing and high-performance computing, dynamic distribution of machine learning-based data processing between edge devices and the cloud
  • security and trustworthiness in the context of AI, homomorphic encryption for privacy-preserving data processing and machine learning, robustness guarantees for deep learning in an adversarial setting
  • AI-based virtual assistants (chatbots), knowledge systems combining visual and semantic information (joint embeddings), Transformer networks and other advanced models for meaning representation, applications of reinforcement learning in agent systems
  • data analytics for extremely large streams of data resulting from cyber-physical  products (esp. vehicles, smart buildings/homes, IoT sensors) and their production, predictive maintenance in smart factories, optimisation of production models, quality control, root cause analysis, supply chain optimisation, digital twins
  • collaborative robotics, augmented reality in robot programming, medical service robots, learning-based adaptation in wearable robots, autonomous vehicles and drones
  • information extraction and machine learning in bioinformatics and public health monitoring, digital humanities, know-how management, brand reputation technologies, mental well-being support, identification of problematic uses of internet, data annotation by means of crowdsourcing, gamification, and games with purpose

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