Hello.

I'm an AI researcher based out of New York City.

I'm a research scientist at Abridge, where I work on post-training and reinforcement learning of foundation models. Previously, I received my Ph.D. in Computer Science from Columbia University, advised by Zhou Yu and Smaranda Muresan. During my PhD, I was an NSF Graduate Research Fellow, an ML Alignment and Theory Scholar, and a frequent research intern at Microsoft, Google, and elsewhere.

I'm currently focused on methods that help foundation models learn from noisy, real-world data to support increasingly agentic use cases. In high-stakes domains like healthcare, my work pushes on methods in evaluation, human feedback, reinforcement learning, and synthetic data.

skywang [at] cs.columbia.edu