Akshay K. Jagadish is a postdoctoral research fellow at the Princeton AI Lab. His work bridges cognitive science and artificial intelligence, taking two complementary approaches to understanding natural and artificial minds:

1. Designing scalable, sub-symbolic models of human and machine cognition through frameworks such as meta-learning, rational analysis, ecological rationality, resource-rationality and bounded ecologically rational analysis.
2. Developing AI-driven methods to uncover interpretable symbolic programs of behavior and the internal representations that guide them.

Before joining Princeton, he spent six wonderful years in Germany, where he earned a Ph.D. in Computer Science and an M.Sc. in Computational Neuroscience at the University of Tübingen under the mentorship of Eric Schulz and Marcel Binz. His research has been featured in popular press, including The New York Times^[1][2], Fortune^[3], Tagesspiegel^[4], and Science Daily^[5].

Key Publications

Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J.X., & Schulz, E. (2024). Meta-Learned Models of Cognition. Behavioral and Brain Sciences.
Jagadish, A. K., Coda-Forno, J., Thalmann, M., Schulz, E., & Binz, M. (2024). Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks. Proceedings of the 41st International Conference on Machine Learning (ICML), Vienna, Austria.
Schubert, J. A., Jagadish, A. K., Binz, M., & Schulz, E. (2024). In-Context Learning Agents Are Asymmetric Belief Updaters. Proceedings of the 41st International Conference on Machine Learning (ICML), Vienna, Austria.
Rmus, M.*, Jagadish, A. K.*, Mathony, M., & Schulz, E. (2025). Generating Computational Cognitive Models using Large Language Models. Advances in Neural Information Processing Systems (NeurIPS), San Diego, USA.
Jagadish, A. K., Binz, M., & Schulz, E. (2025). Meta-learning Ecological Priors from Large Language Models explains Human Learning and Decision making. Under review.