Amy Xiang Wang

Email: xw914@nyu.edu

New York, NY

I am an MS in Computer Science student at NYU Courant and a research associate at NYU Grossman School of Medicine. I am broadly interested in AI for Science, particularly in generative models for scientific discovery, optimal transport, differential geometry, and physics-informed machine learning. In a previous life, I worked as a reporter at a business magazine in NY, covering macroeconomics, tech companies, and US-Asia. I also studied international relations and economics at Johns Hopkins University.

selected publications

ICML workshop

On Conditional Sampling with Joint Flow Matching

Amy Xiang Wang

In ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling , 2024

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SCML

Generalized Lie Symmetries in Physics-informed Neural Operator

Amy Xiang Wang*, Zakhar Shumaylov*, Peter Zaika, Ferdia Sherry, and Carola-Bibiane Schönlieb

Scientific Computing and Machine Learning, Oral Presentation , 2025

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Physics-informed neural operators (PINOs) have emerged as powerful tools for learning solution operators of partial differential equations (PDEs). Recent research has demonstrated that incorporating Lie point symmetry information can significantly enhance the training efficiency of PINOs, primarily through techniques like data, architecture, and loss augmentation. In this work we focus on the latter, highlighting that point symmetries oftentimes result in no training signal, limiting their effectiveness in many problems. To address this, we propose a novel loss augmentation strategy that leverages evolutionary representatives of point symmetries, a specific class of generalized symmetries of the underlying PDE. These generalized symmetries provide a richer set of constraints than standard symmetries, leading to a more informative training signal. We demonstrate that this approach significantly enhances performance of neural operators across a variety of PDEs, achieving improved data efficiency.