
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 like the Cognitive Reflection Test.
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.
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.
Peer-reviewed papers:
You can find my Google Scholar profile here.