“All men by nature desire to know.”
— Aristotle
I study intelligence and human nature to help shape a more flourishing future. My research focuses on building interpretable AI systems capable of understanding and forecasting human behavior.
This work bridges the gap between Machine Learning and the Behavioral Sciences, contributing to two core areas:
- AI Safety: By building systems that operate on clear, interpretable principles, we can align AI with human values and ensure safety in high-stakes settings.
- AI for Science: I use these systems to automate and scale the study of human behavior, generating reliable foresights that equip decision-makers with better judgment.
I am a PhD student in the Machine Learning Group at the University of Toronto, advised by Roger Grosse and Jimmy Ba, and a visiting researcher at Stanford CS with Sanmi Koyejo. I am also a researcher at the Forecasting Research Institute, collaborating with Philip Tetlock, Ezra Karger, and Chris Karvetski on AI judgemental forecasting and hypothesis generation.
My research is built on two pillars of extensive training: the behavioral sciences, which help me frame the core questions, and a solid technical foundation in deep learning, optimization, statistics, and information theory, which provides the tools to answer them. You can find an overview of my past research here.
Community & Industry
I believe this mission is best pursued in community. To that end, I co-founded:
- for.ai: An independent AI research lab acquired by Cohere to become Cohere Labs.
- UTMIST: Now the largest AI student organization at the University of Toronto, creating opportunities for the next generation of AI talent.
For current undergraduates, I have compiled a list of U of T mentorship programs I strongly recommend: Read the guide here.
I always appreciate the opportunity to learn and grow, and I welcome your input.
- Anonymous Feedback: Leave a note here.
- Collaboration: Reach me at
huang [at] cs [dot] toronto [dot] edu.