Khai Loong Aw

I am a Computer Science PhD student at Stanford University.

I am currently working on computer vision and developmental cognitive science in the NeuroAI Lab, advised by Daniel Yamins.

Previously, I worked on identifying methods to train AI language models that improve their functional similarity to the language processing mechanisms in the human brain. In 2023, I worked with Antoine Bosselut and Martin Schrimpf at EPFL. In 2022, I worked with Mariya Toneva at the MPI for Software Systems.

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Research

We propose Local Random Access Sequence (LRAS), an autoregressive generative model architecture. Using optical flow as an intermediate representation, LRAS achieves state-of-the-art novel view synthesis and 3D object manipulation.

We investigate how instruction-tuning affects language models from a neuroscientific perspective, revealing that it generally improves their alignment with human brain activity, with model size and world knowledge playing key roles.

We show that training language models to summarize narratives (i.e., deeper understanding of characters, emotions, and relationships) results in richer representations that are more aligned to human brain activity.