Akshay K. Jagadish is a postdoctoral research fellow at the Princeton AI Lab. His research takes two complementary approaches to study natural and artificial minds:
- Building scalable, sub-symbolic models of human (and machine) cognitive function using frameworks such as meta-learning, ecological rationality, and resource-rationality.
- Developing AI-driven methods to accelerate the discovery of interpretable symbolic programs of human (and machine) behavior and their internal representations.
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. Under review.
Jagadish, A. K., Binz, M., & Schulz, E. (2025). Meta-learning Ecological Priors from Large Language Models explains Human Learning and Decision making. Under review.