Summer Research Internship
Summer Research Intern at the University of Edinburgh School of Informatics. The specific research topic is still being developed.
Research
I am broadly interested in human cognition. Humans have remarkably limited cognitive resources, yet we can show creativity, reasoning, and problem-solving abilities that seem to go far beyond those limits. I hope to draw inspiration from the human mind to help build better AI systems.
Research Interests
- Computational cognitive science
- Cognitive psychology
- Machine learning
- Human-inspired AI
Selected Publications
- Online library learning in human visual puzzle solving Proceedings of the 48th Annual Meeting of the Cognitive Science Society (CogSci 2026). arXiv:2603.23244. [arXiv]
- Prospective Compression in Human Abstraction Learning arXiv:2605.09985. [arXiv]
- Exploring Generative AI Empowerment in City Brand Building from a Human-Factors Perspective: An Empirical Study Based on Wuhan City Emoji Human Factors in Communication of Design, AHFE Conference Proceedings. DOI: 10.54941/ahfe1006335. [Open Access] Presented at the conference by Pinzhe Zhao.
Selected Projects
Online Library Learning in Human Visual Puzzle Solving
Research Assistant at the University of Edinburgh School of Informatics. This project asks how people learn reusable helper abstractions while solving visual puzzles, and how those learned abstractions shape later problem solving. It connects human behavioral data with computational accounts of online library learning.
Prospective Compression in Human Abstraction Learning
This project studies whether people form abstractions not only for the current task, but also in anticipation of future problems. It examines prospective compression in human abstraction learning: how people simplify, reorganize, and reuse learned structure when task demands change.
Brain-Heart Interactions During Sleep
Visiting Student-RA at Shenzhen Medical Academy of Research and Translation. I worked on a multimodal machine-learning pipeline for predicting sinus pause events in mice from EEG, ECG, and sleep-state information. The project involved physiological signal preprocessing, feature extraction, sleep-state-aware analysis, and the training and evaluation of ensemble models for anticipatory risk prediction.
Cognitive Training Transfer and Individual Differences
Voluntary Research Assistant at the Cognitive Ability & Plasticity Lab. I worked on machine-learning models for predicting individual differences in transfer learning from longitudinal cognitive training data. The analysis combined pre-training cognitive profiles, background variables, and task performance measures to model transfer effects, alongside diffusion-modeling parameters estimated with HDDM.