Areas of Work
PhD Project:
"Combining the benefits of distributional and structured semantic representations to address domain-specific NLP Problems."
Recent natural language processing (NLP) research is captivated by large language models (LLMs) based on artificial deep neural networks. These LLMs encode distributional semantic representations derived from massive datasets by pushing the limits of current GPU hardware and TPU chips optimized for neural network training. But for some domain-specific problems, the adoption of such LLMs can be challenging due to sparse training data or lack of interpretability and explainability. My primary interest lies in exploring how classical structured semantic representations such as knowledge graphs in domain specific enterprise contexts, can be combined with these LLMs to address the shortcomings and limitations of both approaches.
CV
Since 03/2023
PhD student at the Chair of Information Science
Since 2016
Software Engineer in the field of search engines and information retrieval (self-employed since 2019)
2012 - 2016
Master's degree in Robotics, Cognition, Intelligence at the Technical University of Munich
2007 - 2012
Bachelor's degree in Media Informatics at Ludwig-Maximilians-Universität Munich
