Anna Penzkofer
Institute for Visualisation and Interactive Systems, University of Stuttgart
Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.033 Google Scholar
Biography
Anna Penzkofer is a PhD student in the Collaborative Artificial Intelligence group since January 2023. She holds a Bachelor’s degree in Computer Science from the University of Stuttgart and a Master’s degree in Computer Science from the Technical University Munich (TUM). She has been doing research in the group since her bachelor's degree at the University of Stuttgart in 2018. Her main research interest is in computational modelling of human behaviour, specifically in visual processing. Her research includes applications of cognitive models in Computer Vision and Reinforcement Learning, as well as the development of new computational models and cognitively-inspired representations.
Personal website https://apenzko.github.io/
Teaching
- 2026
- Fachpraktikum -- Practical Course: Collaborative Artificial Intelligence (Teaching Assistant)Master
- 2025
- Seminar: Advanced Topics in Collaborative AI (Tutor)Master
- 2024
- Machine Perception and Learning (Tutor)Master
- Fachpraktikum -- Practical Course Interactive Systems: Computational Theory of Mind and Cognition (Tutor)Master
- 2023
- Mensch-Computer-Interaktion (Teaching Assistant)Bachelor
Supervision
- 2026
- Deep Learning Tricks for Spiking Object Detection (M.Sc.) Ning Ding
- Modelling Human Brain Activity Using Biologically Inspired Feature Representations (Internship) Federica Tonoli
- Towards SSP Representations: State and Rule Encoders in XLand-MiniGrid (M.Sc.) Patrick Baisch*
- 2025
- Visual Working Memory Study (Hiwi Project) Patrick Baisch
- Solving KidsARC with Cognitively-inspired Representations (B.Sc.) Martin Nagel
- Energy-Efficient Semantic Segmentation using Spiking Neural Networks (M.Sc.) Arjit Yadav
- 2024
- Atari-YOLO: Generating a Synthetic Dataset for Object Detection in Game Environments (Hiwi Project) Patrick Baisch
- Cognitively-plausible Mental Image Representations (B.Sc.) Sawelij Mödinger
- Neural Reasoning with Cognitively-inspired Representations (B.Sc.) Levi Otterbach+
- Leveraging Biologically-plausible Representations for Robust and Efficient Generalization in Reinforcement Learning (M.Sc.) Nan Jiang*
- 2023
- Evaluation of Different Image Representations for Reinforcement Learning Agents (M.Sc.) Jayakumar Ramasamy Sundararaj*
- Biologically Plausible Reinforcement Learning (B.Sc.) Tobias Stegmaier
- An Adaptive Model of Gaze-based Selection (INFOTECH Project) Jayakumar Ramasamy Sundararaj
