Publications

Fine-grained Urban Heat Island Effect Forecasting: A Context-aware Thermodynamic Modeling Framework
Fine-grained Urban Heat Island Effect Forecasting: A Context-aware Thermodynamic Modeling Framework

Xingchen Zou, Weilin Ruan, Siru Zhong, Yuehong HU, Yuxuan Liang

Under review. 2025

We present DeepUHI, a heat equation-based framework that models urban heat island effects through thermodynamic cycles and thermal flows, integrating multimodal environmental data to achieve precise street-level temperature forecasting, now deployed as a real-time warning system in Seoul.

Paper Code Urban Computing Spatio-temporal Graph Learning

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Siru Zhong, Weilin Ruan, Ming Jin, Huan Li, Qingsong Wen, Yuxuan Liang

Under review. 2025

This paper proposes Time-VLM, a novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for enhanced time series forecasting.

Paper Time Series Multimodal

LDM4TS: Latent Diffusion Model for Time Series Forecasting
LDM4TS: Latent Diffusion Model for Time Series Forecasting

Weilin Ruan, Siru Zhong, Haomin Wen, Yuxuan Liang

Under review. 2025

This paper introduces LDM4TS, a novel latent diffusion model for time series forecasting that transforms time series into multiple image representations and leverages diffusion models to enhance forecasting capabilities.

Paper Time Series Multimodal

A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation

Songxin Lei, Qiongyan WANG, Yanchen ZHU, Hanyu Yao, Sijie Ruan, Weilin Ruan, Yuyu Luo, Yuxuan Liang

Under review. 2024

We introduce GSTRL, a novel game-theoretic reinforcement learning framework that addresses collaborative public resource allocation by modeling it as a cooperative potential game and incorporating spatio-temporal learning to capture crowd dynamics, outperforming existing methods on real-world datasets.

Paper Code Reinforcement Learning Spatio-temporal

Kinematics-based Object Articulation with Gaussian Splatting
Kinematics-based Object Articulation with Gaussian Splatting

Weichuang Li, Weilin Ruan, Xuechao Zhang, Yuxuan Liang

Under review. 2024

We present a novel approach that combines Gaussian Splatting with kinematic knowledge to reconstruct and simulate articulated objects, enhancing both geometric detail and articulation dynamics while overcoming the limitations of previous implicit-based models.

Paper Computer Vision

Low-rank Adaptation for Spatio-Temporal Forecasting
Low-rank Adaptation for Spatio-Temporal Forecasting

Weilin Ruan, Wei Chen, Xilin Dang, Jianxiang Zhou, Weichuang Li, Xu Liu, Yuxuan Liang

Under review. 2024

This paper presents ST-LoRA, a novel low-rank adaptation framework as an off-the-shelf plugin for existing spatial-temporal prediction models, which alleviates node heterogeneity problems through node-level adjustments while minimally increasing parameters and training time.

Paper Spatio-temporal

Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting
Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

Weilin Ruan, Wenzhuo Wang, Siru Zhong, Wei Chen, Li Liu, Yuxuan Liang

Under review. 2024

This paper proposes a novel spatio-temporal unitized model for traffic flow forecasting that effectively captures complex dependencies across both space and time dimensions, achieving state-of-the-art performance on multiple benchmark datasets.

Paper Spatio-temporal