Kyle Hsu

Kyle Hsu
徐宏愷

PhD student

Stanford University

kylehsu at cs dot stanford dot edu


Hi! I’m a second-year computer science PhD student at Stanford University, where I work as a member of the Stanford AI Lab. I’m advised by Chelsea Finn and Jiajun Wu and affiliated with the StatsML and SVL groups. My research and education is generously supported by a Sequoia Capital Stanford Graduate Fellowship. I’m a proud Taiwanese-Canadian.

As you might be able to tell from my publications, I have a broad research interest in AI. However, I try to ground my interests in the grand challenge of endowing robots with key qualities of intelligent behavior. Towards this end, I enjoy thinking about a diverse array of topics, including out-of-distribution generalization, vision, reinforcement learning, meta-learning, meat-learning, planning, self-supervised learning, Bayesian inference, and communication.

Previously, I majored in robotics as an Engineering Science undergraduate at the University of Toronto. During this time, I did research at the Vector Institute with Roger Grosse and Dan Roy. I’ve also spent time at Google Brain with Shane Gu, Berkeley AI Research with Sergey Levine, and the Max Planck Institute for Software Systems with Rupak Majumdar. My first research experiences were in optoelectronics and photonics under Joyce Poon at the University of Toronto and Ming C. Wu at UC Berkeley.

In my spare time, I like to sing Mandopop ballads, play board games, and pretend to be a philosopher.

other publications

On the Opportunities and Risks of Foundation Models

Foundation models are pre-trained, self-supervised, large-scale, multi-modality models that exhibit emergent functionalities and homogenize deep learning models.

Lazy Abstraction-Based Control for Safety Specifications

Controller synthesis for safety specifications with on-demand abstraction construction.

Multi-Layered Abstraction-Based Controller Synthesis for Continuous-Time Systems

Efficient correct-by-construction controller synthesis via adaptive spatiotemporal granularity.

Germanium Wrap-Around Photodetectors on Silicon Photonics

Wrapping germanium photodetectors around silicon waveguides results in devices with improved optoelectronic properties.