I study how the brain organizes and represents “meaning”—how words, memories, and concepts are encoded, reorganized, and transformed in the nervous system. My goal is to understand the neural mechanisms of semantic memory and to explore the extent to which they can be reverse-engineered.

I believe that understanding the neural representation of meaning is central to linking consciousness, cognition, and communication. This line of work can clarify how cognition is organized and may contribute to narrowing the information gap and to more direct, cross-lingual knowledge transfer.

Current directions:

• Interpretable temporal neural modeling: Building models that identify “semantic transitions” or critical cognitive nodes in neural time series, with human interpretability as a core constraint.
• LLM–brain semantic alignment: Comparing large language model embeddings with neural responses to meaningful stimuli to explore a shared semantic representation space for brain–computer communication or semantic-level neural translation.

I view the brain as a structured system that can be modeled systematically, and I aim to reveal how thought is organized, constrained, and transformed.

If you work on neural semantics, semantic alignment, cognitive modeling, or information equity, I’d be glad to connect. zy1001@berkeley.edu. Full background: CV; publications: Google Scholar.