Hey, I'm Winnie

Let's reflect on the latest scientific research, design pluralistic implementations, and use technology to empower expansive reformation.

My research lies at the intersection of alignment science, generative models, and natural language understanding. My goal is to build scalable AI systems that are shaped by real-world actors and feedback. Towards this, I explore ways to inject classical algorithms with differentiable components, leverage (increasingly) strong LLMs in synthetic data pipelines, and improve the factual reasoning capabilities of weaker models.

I'm fortunate to have learned from many influential mentors at the early stages of my career. Most recently, I was a Student Researcher at Facebook AI Research (Meta AI), and collaborated closely with Stefano Ermon and friends at Stanford StasML. In my undergrad, I worked on various projects at Google DeepMind with Igor Mordatch (Brain Robotics) / David Dohan (Generative Models) / Durk Kingma.

I studied Artifical Intelligence Computer Science, Statistics and Math at the University of Toronto. My research journey began by scaling methods in latent variable modeling and probabilistic inference with David Duvenaud (Vector Institute). I hope to one day bring similar influence with my ideas at the cutting edge.

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2019 xwinxu's Github chart

Winnie Xu 2022.