My ultimate research goal is to explore how human and machine develop fluid intelligence—the ability to reason, solve problems, and adapt to novel scenarios. Grounded in semantic externalism and pragmatism, the main themes of my research are concepts and symbols, from embodied and pragmatic angles. How concepts and symbols emerge and evolve through interaction with environments, regardless of their origin (i.e., neural architectures, neural-symbolic models, or the human nervous system)? How agents leverage them for reasoning and problem solving?

As a machine learning researcher, I aim to build machines capable of compositional and generalizable concept learning from few-shot examples as well as efficient, adaptive inference in test-time compute. As a cognitive scientist, I investigate the mechanisms of shared cognition between humans and machines, and the fundamental differences in the architecture and reasoning processes.

• Teaching Assistant, NTU EE1006: Cornerstone EECS Design and Implementation, Spring 2022 / Spring 2023
  
• Teaching Assistant, NTU EE1004: Computer Programming, Fall 2021
  
• Teaching Assistant, NTU EE4041: Digital System Design, Spring 2019
  

This is my academic page, following the convention to keep things professional. Visit here to learn more about me.