
At the end phase of my Ph.D. in psychology, I trained myself in various machine learning techniques, especially artificial neural networks. To consolidate and advance these skills, I expanded my research focus and added a post-doc on computational modeling approaches to my career. During my time at the Neuro-Cognitive Modeling lab of Prof. Martin Butz, I investigated how artificial neural networks can be improved by including inductive biases that are inspired by human cognition and applied the empirical rigor that I had acquired during my Ph.D. to investigate the models’ inner working mechanisms.
I was a visiting scholar at Prof. Virginia de Sa’s lab at the Halıcıoğlu Data Science Institute at UC San Diego, where I applied a computer vision model to investigate a psychological research question, supported by a fellowship from the Science department of the University of Tübingen. Additionally, I was visiting Stanford University to investigate machine intuition in large language models with the help of psychological methods, resulting in a Nature paper.
My past experience led to my current role at Mercedes-Benz AG, where I will be developing the Mercedes Virtual Assistant as an AI Architect.
Peer-reviewed papers:
• Hagendorff, T. & Fabi, S. (2023). Why we need biased AI – How including cognitive biases can enhance AI systems. Journal of Experimental & Theoretical Artificial Intelligence.
Media appearances:
Presentations:
• Fabi, S. (2022). Applying Cognitive Science to Machine Learning and vice versa. Research Talk, DeepMind, London.
• Fabi, S. (2022). Efficient learning in generative RNNs: Solving one-shot tasks by including compositionality as an inductive bias. Tech Talk, Amazon, Tübingen.
• Fabi, S. (2022). Machine learning for psychological research: Using the example of the racial bias in pain recognition. Machine Learning in Science: Postdoc Symposium of the Cluster of Excellence, Tübingen.
• Fabi, S., Otte, S. & Butz, M.V. (2021). Fostering compositionality in generative RNNs to solve the Omniglot challenge. Oral presentation at the Computational Cognition Workshop.
• Fabi, S., Otte, S., & Butz, M.V. (2021). Fostering compositionality in latent, generative encodings to solve the Omniglot challenge. Oral presentation at the 30th International Conference on Artificial Neural Networks (ICANN).
• Fabi, S., Otte, S. & Butz, M.V. (2021). Does compositionality as a prior in Generative RNNs lead to efficient learning of temporal predictions?. Oral presentation at the ICDL Workshop Spatio-temporal Aspects of Embodied Predictive Processing.
• Fabi, S., Otte, S., & Butz, M.V. (2021). Compositionality as learning bias in generative RNNs solves the Omniglot challenge. Poster presented at International Conference on Learning Representations (ICLR) – Workshop Learning to Learn.
You can find my Google Scholar profile here.