About Me

I’m an AI research scientist at Meta Superintelligence Labs. I received my Ph.D. in Computer Science from University of Toronto. where I was advised by Yang Xu.

My current research centers on multimodal AI agent systems powered by large language models. I explore how these agents can reason, memorize, plan, and act for real-world tasks, and how they could adaptively infer and fulfill user needs. My research draws on techniques such as reinforcement learning, memory-augmented generation, and world models.

I’m also committed to making AI reliable and transparent. I have designed algorithms and tools that improve the interpretability, safety, and trustworthiness of large language models. I’m currently focused on understanding how large language models acquire world knowledge and reasoning capability from vast amounts of data during pre-training and post-training.

During my PhD, I studied automated understanding and generation of creative language use, and I was fortunate to intern at Meta FAIR and Google Research.