I research scalable and principled methods for aligning large generative models. In the past, I worked at FAIR Labs at Meta, Google DeepMind and Stanford AI. I'm a graduate of CS, Math, and Stats at the University of Toronto where I began learning about latent variable models and probabilistic inference at the Vector Institute. I'm open to full-time ML research and engineering roles.

Like my models, I'm still learning (: