About
I am a PhD candidate in the Computational Principles of Intelligence Lab at the Max Planck Institute for Biological Cybernetics.
Previously, I had been a student of physics and mathematics to appreciate the beauty of the outstanding scientific achievements in history. I was fortunate to live and interact with multiple cultures and nations, along which I found my passion for computational cognitive neuroscience.
My research focuses on studying intelligence manifested in patterns and abstractions. We seamlessly discover repeated patterns and abstract structures from perceptual sequences, which helps us to create and learn languages, enjoy music, do mathematics, organize thoughts, reason, imagine, and plan. I build computational models to capture the process of learning structured representations from sequential data, and conduct experiments to test the hypotheses implicated in behavioral and neural activities.
Aside from research, I commit to a mindful life that is balanced and rich with meaning on an individual and community level. I seek to contribute to collective growth, fulfillment, collaboration, and mutual empowerment in living and scientific communities. I love nature, art, and humanities, and at times pondering over social issues.
Currently, I am looking for a post-doc or an industry position on the topic of AI alignment/interpretability, computational cognitive/neuroscience, or behavioral/neural data analysis.
Contact
shuchen.wu at tuebingen.mpg.de
Publications
Wu, S. C., Thalmann, M., Schulz, E. (2023). Motif Learning Facilitates Sequence Memorization and Generalization. PreprintSchreiber, A., Wu, S. C., Wu, C. X., Indiveri, G., Schulz, E. (2023). Biologically-plausible hierarchical chunking on mixed-signal neuromorphic hardware. NeurIPS 2023 Workshop on Machine Learning with New Compute Paradigms
Wu, S. C., Élteto, N.,Dasgupta, I., & Schulz, E. (2023). Chunking as a rational solution to the speed–accuracy trade-off in a serial reaction time task. Scientific Reports
Wu, S. C., Élteto, N.,Dasgupta, I., & Schulz, E. (2022). Learning Structure from the Ground-up—Hierarchical Representation Learning by Chunking. 36th Conference on Neural Information Processing Systems (NeurIPS 2022).