Yannick Metz
I am a postdoctoral researcher working on Human-AI Communication and Reinforcement Learning from Human Feedback at ETH Zürich.
Research Interest: Reinforcement Learning from Human Feedback, Human-AI Interaction, Explainable AI, Visual Analytics
I am a research assistant at the ETH IVIA Lab under Prof. Mennatallah El-Assady. Until recently, I was a PhD student at the University of Konstanz, advised by Prof. Daniel Keim. I was also a visiting researcher at Worcester Polytechnic Institute (WPI) in the US in 2024. I work on Human-AI Communication, combining reinforcement learning from human feedback, interactive visualizations for explainability, and uncertainty in RL.
My Research
My main research aim is the improvement of bidirectional communication between humans and AI agents for effective learning and alignment. In particular, I focus on the following topics:
- Widening the space of human feedback for RL: Going beyond preference learning, and towards diverse, expressive, contextual feedback.
- Reinforcement Learning from Human Feedback: Integrating different feedback types into the reward learning pipeline.
- Interactive Visualizations for Explainability: Understanding RL agent behavior allows for better feedback and more trustworthy interactions.
- Uncertainty in Reinforcement Learning: Measuring an agent's uncertainty is crucial for effective human-AI communication.
Publications
[1] Raphaël Baur, Yannick Metz, Maria Gkoulta, Mennatallah El-Assady, Giorgia Ramponi, Thomas Kleine Buening MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
ArXiv Preprint, 2026
[2] Timo Kaufmann, Yannick Metz, Daniel Keim, Eyke Hüllermeier ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
The Annual Conference on Neural Information Processing Systems (NeurIPS), Conference Paper, 2025
[3] Yannick Metz, András Geiszl, Raphaël Baur, Mennatallah El-Assady Reward Learning from Multiple Feedback Types
International Conference on Learning Representations (ICLR), Conference Paper, 2025
[4] Yannick Metz, David Lindner, Raphaël Baur, Mennatallah El-Assady Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
ArXiv Preprint, Survey Paper, 2024
[5] Yannick Metz, David Lindner, Raphaël Baur, Daniel A. Keim, Mennatallah El-Assady RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
ICML2023 Interactive Learning from Implicit Human Feedback Workshop, Full Paper, 2023
[6] Yannick Metz, Eugene Bykovets, Lucas Joos, Daniel A. Keim, Mennatallah El-Assady Visitor: Visual interactive state sequence exploration for reinforcement learning
Full Paper (Eurovis), Computer Graphics Forum, 2023
[7] Yannick Metz, Udo Schlegel, Daniel Seebacher, Mennatallah El-Assady, Daniel A. Keim A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
Short Paper, EuroVA, 2022
[8] Eugene Bykovets, Yannick Metz, Mennatallah El-Assady, Daniel A. Keim, Joachim M Buhmann How to Enable Uncertainty Estimation in Proximal Policy Optimization
ArXiv Preprint, 2022
[9] Dirk Streeb, Yannick Metz, Udo Schlegel, Bruno Schneider, Mennatallah El-Assady, Hansj{"o}rg Neth, Min Chen, Daniel A. Keim Task-based visual interactive modeling: Decision trees and rule-based classifiers
IEEE Transactions on Visualization and Computer Graphics, 2021
[10] Yannick Metz, Dennis Ackermann, Daniel A. Keim, Maximilian T Fischer Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent
Visualization in Data Science (VDS at IEEE VIS), 2024
[11] Maximilian C Hartmann, Moritz Schott, Alishiba Dsouza, Yannick Metz, Michele Volpi, Ross S Purves A text and image analysis workflow using citizen science data to extract relevant social media records: Combining red kite observations from Flickr, eBird and iNaturalist
Ecological Informatics
[12] Mennatallah El-Assady, Rebecca Kehlbeck, Yannick Metz, Udo Schlegel, Rita Sevastjanova, Fabian Sperrle, Thilo Spinner Semantic color mapping: A pipeline for assigning meaningful colors to text
2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)
[13] Yannick Metz Effective Imitation and Reinforcement Learning Through Visual Analytics
Masters Thesis
[14] Yannick Metz Evaluating Concept Attributions for the Interpretability of Deep Neural Networks
Bachelor Thesis
Teaching
- TA: Data Mining: Basic Concepts (UKON) - WS 2022/23, WS 2023/24, and WS 2025/26
- TA: Seminar: Data Visualization and Analysis (UKON) - WS 2024/25
- TA: Data Visualization (UKON) - SS 2023
- TA: Natural Language Processing/Document Analysis (UKON) - SS 2022
- TA: Programming Course: Object-Oriented Programming (UKON) - WS 2021/22 and SS 2024
Education
I have a M.Sc. and B.Sc. in Computer Science from the University of Konstanz, Germany. I have also studied abroad in Uppsala, Sweden for a year. During my studies, I focused on machine learning, particularly explainable AI and reinforcement learning. During my studies, I worked for 2 years as a working student at Airbus Defence and Space in Immenstaad, Germany. I have also worked as a student research assistant at the University of Konstanz.