Constantin Ruhdorfer

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.035 Google Scholar

Biography

Constantin Ruhdorfer is a PhD student in the Perceptual User Interfaces group and a PhD scholar in the International Max Planck Research School for Intelligent Systems (IMPRS-IS) since November 2023. He holds a Bachelor’s degree in Computer Science from the Cooperative State University Baden-Württemberg and a Master's degree in Computer Science from the University of Stuttgart. His research interests lies in building and training agents that can cooperate well with humans by taking into account their individual mental states and preferences. Likewise his interests lie at the intersection of multi-agent reinforcement learning, inverse reinforcement learning, machine theory of mind and AI alignment.

Teaching

2023

Machine Perception and Learning (Tutor) (winter)Master

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Supervision

2024

The Yokai Challenge: A New Frontier for Multi-Agent Reinforcement Learning and Machine Theory of Mind (M.Sc.) Rui Yang

2023

Evaluation of Different Image Representations for Reinforcement Learning Agents (M.Sc.) Jayakumar Ramasamy Sundararaj*

*Co-supervised with Anna Penzkofer

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Open Thesis Projects

 

Cognitive Attention Guided Imitation LearningMaster

 

Generalization in Reinforcement Learning via a Human Attention OracleMaster

 

Intelligent Autocurricula for Generalization in Cooperative Multi-Agent Reinforcement Learning EnvironmentsMaster

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Publications

(Wang et al., 2024)

1. 

   VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour

   Yao Wang, Yue Jiang, Zhiming Hu, Constantin Ruhdorfer, Mihai Bâce, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8(ETRA), pp. 1–18, 2024.

   Abstract Links BibTeX Project

   Question answering has recently been proposed as a promising means to assess the recallability of information visualisations. However, prior works are yet to study the link between visually encoding a visualisation in memory and recall performance. To fill this gap, we propose VisRecall++ – a novel 40-participant recallability dataset that contains gaze data on 200 visualisations and five question types, such as identifying the title, and finding extreme values.We measured recallability by asking participants questions after they observed the visualisation for 10 seconds.Our analyses reveal several insights, such as saccade amplitude, number of fixations, and fixation duration significantly differ between high and low recallability groups.Finally, we propose GazeRecallNet – a novel computational method to predict recallability from gaze behaviour that outperforms several baselines on this task.Taken together, our results shed light on assessing recallability from gaze behaviour and inform future work on recallability-based visualisation optimisation.

   doi: 10.1145/3655613

   Paper: wang24_etra.pdf

   Supplementary Material: wang24_etra_sup.pdf

   @article{wang24_etra, title = {VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour}, author = {Wang, Yao and Jiang, Yue and Hu, Zhiming and Ruhdorfer, Constantin and Bâce, Mihai and Bulling, Andreas}, year = {2024}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, pages = {1--18}, volume = {8}, number = {ETRA}, doi = {10.1145/3655613} }