Kaiqu Liang

I am a second year PhD student in Computer Science at Princeton University, advised by Jaime Fernández Fisac.

Previously, I completed my MPhil in Machine Learning at the University of Cambridge, advised by Samuel Albanie and Bill Byrne. I did my undergraduate studies at the University of Toronto, where I was advised by Roger Grosse and Sven Dickinson. During my undergraduate years, I was also a student researcher in Vector Institute, advised by Roger Grosse.

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Research

I'm working on AI safety, focusing on human-AI alignment and uncertainty in foundation model. I also did research on video retrieval and out-of-distribution generalization.

We propsoed introspective planning as a systematic method for guiding LLMs in forming uncertainty-aware plans for robotic task execution.

We introduced Branch and Play (B&P), an algorithm that effectively resolves multi-agent spatial navigation problems by determining the optimal order of play.

We proposed several simple yet effective baselines for interactive video retrieval via question-answering.

We demonstrated that equilibrium model improves generalization in harder instances due to their path independence, highlighting its importance for model performance and scalability.

We demonstrated and discussed why Deep Equilibrium (DEQ) Models outperform fixed-depth counterparts in generalizing under distribution shifts.

Education

Teaching

Reviewer services

Website source from Jon Barron