Prof. Qihong Lu (呂其鴻)

Prof. Qihong Lu has joined CityU Neuroscience since January 2026. He obtained his BS in Mathematics and Psychology from the University of Wisconsin-Madison. He earned his PhD in Cognitive Psychology at Princeton University, where he worked on neural network models of how humans use episodic memory in naturalistic paradigms with Kenneth A. Norman and Uri Hasson. Then, he joined the Center for Theoretical Neuroscience at Columbia University as an Alan Kanzer Postdoctoral Fellow, where he expanded his modeling work to memory-based decision-making with Daphna Shohamy and Stefano Fusi.

Research Interest

Episodic memory (EM) enables us to rapidly encode information upon a single exposure and retrieve it later, which is essential for adaptive memory-based behavior in humans. Yet, many fundamental questions remain unclear, and EM might be an important missing piece in advancing modern AI. How does the brain use EM to support cognition? How does the brain coordinate EM with other memory systems? What’s the optimal coordination strategy, and to what extent does the human brain implement this optimal strategy?

We use artificial neural networks as model organisms to investigate computational principles of learning and memory, and then use human behavioral and neuroimaging experiments to test our model predictions. By reverse-engineering human memory in neural network models, we can gain insight into the cognitive architecture the brain employs and the statistical structure of the environment we live in (1–5).

Understanding the computational mechanisms of human memory would make timely contributions to i) effective algorithms for NeuroAI, ii) better solutions to memory-related issues (e.g., brain fog, cognitive aging, Alzheimer's disease), and iii) informed principles of learning and education.

Position Availability

I have RA, PhD, and postdoc positions available! Come work with me on neural network models and behavioral/neuroimaging experiments on human memory and NeuroAI! Please take a look at my info page if you are interested in working together with me!

Representative Publications

1. Lu, Q., Hasson, U., & Norman, K. A. (2022). A neural network model of when to retrieve and encode episodic memories. eLife, 11, e74445.
2. Lu, Q., Nguyen, T., Zhang, Q., Hasson, U., Griffiths, T. L., Zacks, J. M., Gershman, S., & Norman, K. A. (2024). Reconciling shared versus context-specific information in a neural network model of latent causes. Scientific Reports, 14(1), 1–15.
3. Lu, Q., Hummos, A., & Norman, K. A. (2024). Episodic memory supports the acquisition of structured task representations. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.
4. Lu, Q., Norman, K. A., & Shohamy, D. (2024). A Normative Account of the Influences of Contextual Familiarity and Novelty on Episodic Memory Policy. Conference on Cognitive Computational Neuroscience.
5. Dong, C. V., Lu, Q., Norman, K. A., & Michelmann, S. (2025). Towards large language models with human-like episodic memory. Trends in Cognitive Sciences.

5 January 2026