Yuan Yuan (苑 苑)

I am currently a postdoctoral researcher at Courant Institute of Mathematical Sciences, New York University, working with Prof. Carlos Fernandez-Granda and Prof. Laure Zanna, and as a member of Multiscale Machine Learning In Coupled Earth System Modeling (M²LInES). Previously, I completed my PhD at FIBLAB, Department of Electronic Engineering, Tsinghua University, advised by Prof. Depeng Jin and Prof. Yong Li. I received my bachelor degree from the Department of Electronic Engineering, Tsinghua University in 2020.

My research centers on developing scalable artificial intelligence methodologies for real-world spatiotemporal systems, with applications in climate dynamics, urban systems, and energy infrastructures. I aim to build robust foundation models that capture complex interdependencies across spatial and temporal scales, enabling intelligent reasoning and decision-making in dynamic and uncertain environments.

Research Interests

• Spatio-temporal Learning: AI foundation models for Earth systems, spatio-temporal foundation models, diffusion models for spatio-temporl data

• Urban Computing: Mobility foundation models, Urban spatiotemporal foundation models

News

Selected Publications

Google scholar for full list.

UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction

We build a universal model for general spatio-temporal prediction and show the benefits of a one-for-all solution in urban contexts.

Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation

This diffusion-based framework performs generative pre-training on a collection of model parameters. By generating customized model parameters, we manage to address spatio-temporal few-shot learning.

Spatio-temporal Diffusion Point Processes

We develop a diffusion model to learn spatio-temporal point processes.

Learning to Simulate Daily Activities via Modeling Dynamic Human Needs

We introduce the modeling of dynamic human needs into activity simulation.

Activity Trajectory Generation via Modeling Spatiotemporal Dynamics

ActSTD captures spatiotemporal dynamics underlying activity trajectories by leveraging neural differential equations.

Design and source code from Jon Barron's website