Yongli Mou
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Contact Information:
Lehrstuhl Informatik 5
Building: E2
Room: 6208
Ahornstr. 55, 52074 Aachen
I am Ph.D. candidate advised by Prof. Dr. Stefan Decker and co-supervised by Prof. Dr. Oya Beyan and work as a research assistant at the Chair of Computer Science i5 Information Systems and Databases (Informatik 5, DBIS) at RWTH Aachen University since July 2020. I received my master’s degree in Computer Science in 2020 from RWTH Aachen University. My research interests are in the field of Federated Learning, Data Privacy, and Blockchain applications.
- Knowledge Graph Construction from Biomedical Literature using Large Language Models (Running)
- An Empirical Study of Open Source Large Language Models (OSLLMs) (Running)
- Enhancing Knowledge Graph Embedding with Uncertainty Modeling using Fuzzy Logic (Running)
- Consistency-checking German Pathology Reports using Large Language models (Running)
- Development of Chrome extension for Writing Improvement based on Language Models (Running)
- Scientific Question Answering using Retrieval-Augmented Large Language Models (Running)
- Analysing Policy Documents using AI-based Large Language Models (LLMs) (Running)
- Analysing Scientific Publications using AI-based Large Language Models (LLMs) (Running)
- MuCo – Music Composer: Can AI Rival Human Creativity? (Running)
- Adversarial Attacks against Face Detection Systems (Running)
- Knowledge and Social Context-enhanced Fake News Detection (Finished)
- Mastering the Game of Contract Bridge: Strategy Optimization via Deep Reinforcement Learning (Finished)
- Improved Bottom-up Deep Learning Approach for Neuronal Cell Instance Segmentation (Finished)
- Membership Inference Attacks against Generative Models and Differential Privacy Defense (Finished)
- Personalized Federated Hetero-task Learning on Graph Data (Finished)
- Decentralized Identity and Access Management for Distributed Machine Learning Systems (Finished)
- Bridging the gap between design and deployment of statistical analyses in Distributed Analytics (Finished)
- Defense against Data Poisoning Attacks in Federated Learning (Finished)
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