team

Michael Färber is a full professor (W3) at ScaDS.AI, TU Dresden, Germany, where he leads the research group Scalable Software Architectures for Data Analytics. He also heads the Cognitive AI Unit at ScaDS.AI. His research focuses, among other topics, on trustworthy AI, large language models (LLMs), knowledge graphs, retrieval-augmented generation, and AI for science.

CV 📧 michael.faerber@tu-dresden.de 📚 Google Scholar

Shuzhou Yuan’s research interests include natural language processing, large language models, and graph neural networks, with recent work spanning graph-guided reasoning and explanation generation, multilingual and psycholinguistic analysis of LLMs, bias and hate speech in language models, and efficient model adaptation. He has published at venues such as ACL, NAACL, EMNLP, AACL, EACL, and KDD.

📧 shuzhou.yuan@tu-dresden.de 📚 Google Scholar 🔗 Personal Website

Zhan Qu’s research interests lie in explainable AI, machine learning, natural language processing, and graph neural networks. His recent work spans counterfactual and interpretable methods for dynamic graphs, explainable machine learning for aviation safety, and large language models for structured and temporally evolving data. He has published at venues such as ICML, KDD, AAAI, and ACL.

📧 zhan.qu@tu-dresden.de 📚 Google Scholar Profile

Tobias Schreieder’s research interests lie in the areas of natural language processing, information retrieval, trustworthy AI and privacy. With a focus on evidence-based text generation with LLMs, he develops methods that allow users to trace LLM-generated content back to their underlying sources through citations.

📧 tobias.schreieder@tu-dresden.de 📚 Google Scholar

AFM Mohimenul Joaa’s research interests focus on Natural Language Processing combined with Knowledge Graphs, e.g., universal knowledge graph retrieval to support and augment large language models (LLMs).

📧 a_f_m_mohimenul.joaa@mailbox.tu-dresden.de 📚 Google Scholar

Jingbo He’s research interests lie in natural language processing, knowledge graphs, and retrieval-augmented generation, with a focus on knowledge graph-enhanced reasoning for scientific question answering. His current focus is on integrating structured knowledge from academic knowledge graphs with RAG systems to improve evidence retrieval, citation quality, and answer verifiability.

📧 jingbo.he@tu-dresden.de 📚 Google Scholar

Nicholas Popovic’s research interests lie in natural language processing and machine learning, with a particular focus on representation learning, information extraction, and natural language inference. His work spans document-level relation extraction, named entity recognition, synthetic data for information extraction, and interpretable fact decomposition for robust inference, with publications at venues such as NAACL, SemEval, EMNLP, and ACL workshops. He is currently in the final phase of his PhD.

📧 nicholas.popovic@tu-dresden.de 📚 Google Scholar 🔗 Personal Website

Parisa Aghdam’s research interests lie in the areas of machine learning, natural language processing and data science. Currently, she is working on scientific text summarization and simplification. She has contributed to publication such as “SimplifyMyText: An LLM-Based System for Inclusive Plain Language Text Simplification”.

📧 parisa.aghdam@mailbox.tu-dresden.de 📚 Google Scholar

Tim Schopf was a postdoctoral researcher in Michael Färber’s group from 01/2025 to 02/2026. He is now affiliated with the National Institute of Informatics (NII) in Tokyo, Japan. His research interests include natural language processing, knowledge graphs, LLMs for scientific reasoning, attributed scientific text generation, and AI agents for scientific discovery.

📚 Google Scholar 🔗 Personal Website

Chen Shao’s research interests lie in machine learning and graph neural networks, with applications in materials science, optical communication systems, and time series forecasting. Her publications include work on graph neural networks for materials science and chemistry, machine learning-based equalization for optical systems, and robust electricity forecasting. She is affiliated with the Karlsruhe Institute of Technology (KIT), Germany.

📧 chen.shao2@kit.edu 📚 Google Scholar

Tarek Saier’s research interests lie in natural language processing and machine learning, with a particular focus on scientific text mining, citation analysis, and scholarly data infrastructure. His work includes contributions such as unarXive, cross-lingual citation analysis, hyperparameter information extraction from scientific publications, and metadata extraction for scholarly documents. He completed his PhD in 2024 under Michael Färber’s main supervision at KIT and now works in Japan.

📚 Google Scholar