Emmanouil Panagiotou M.Sc.

Emmanouil Panagiotou is a research assistant at the Chair of Open Source Intelligence and a member of the AIML research group at the Research Institute CODE, Universität der Bundeswehr München. He is pursuing a PhD in Computer Science at Freie Universität Berlin under the supervision of Prof. Dr. Eirini Ntoutsi.

His research focuses on explainable, generative, and trustworthy artificial intelligence. His interests include counterfactual explanations, fairness-aware synthetic data generation, learning from scarce and imbalanced data, multi-objective optimisation, explainability for large language models, and automatic prompt optimisation. His work combines methodological machine learning research with applications in structural engineering, digital twins, financial AI, and the evaluation of foundation models for safety-critical environments.

Within the DFG-funded Collaborative Research Centre CRC 1463, he develops generative and explainable AI methods for structural-engineering design and digital-twin simulations. He also contributes to HEGEMON, where he works on the holistic evaluation of generative foundation models for safety-critical applications.

Recently Published Works

• TABFAIRGDT: A Fast Fair Tabular Data Generator Using Autoregressive Decision Trees. Emmanouil Panagiotou, Benoît Ronval, Arjun Roy, Ludwig Bothmann, Bernd Bischl, Siegfried Nijssen, and Eirini Ntoutsi. IEEE International Conference on Data Mining (ICDM), 2025. [paper]
• Generative AI-Augmented Offshore Jacket Design: Integrated Approach for Mixed Tabular Data Generation under Scarcity and Imbalance. Emmanouil Panagiotou, Han Qian, Steffen Marx, and Eirini Ntoutsi. Automation in Construction, 2025.
• TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE. Emmanouil Panagiotou, Manuel Heurich, Tim Landgraf, and Eirini Ntoutsi. ACM International Conference on AI in Finance (ICAIF), 2024. Nominated for the Best Paper Award. [paper]
• Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study. Emmanouil Panagiotou, Arjun Roy, and Eirini Ntoutsi. Workshop on Bias and Fairness in AI at ECML PKDD, 2024. [paper]
• Learning Impartial Policies for Sequential Counterfactual Explanations Using Deep Reinforcement Learning. Emmanouil Panagiotou and Eirini Ntoutsi. Dynamic Explainable AI Workshop at ECML PKDD, 2023. [paper]

See the complete publication list on Google Scholar.

Postal address:
Universität der Bundeswehr München
Forschungsinstitut CODE
Werner-Heisenberg-Weg 39
85577 Neubiberg

Visitor address:
Forschungsinstitut CODE
Carl-Wery-Str. 18-22
81739 München