Big Data Track
This track showcases the forefront of urban Big Data for smart cities. Our broad emphasis remains on exploring how data-centric approaches can support the development of smart cities. This year, we delve into the revolutionary potential of Large Language Models (LLMs) in spatio-temporal data mining for enhanced decision-making and urban planning , alongside graph-based intelligence for real-time urban sensing, synthetic data generation, and fair recommender systems. We will also explore social simulation using LLM-powered agents to model human behaviors and evaluate urban policies, delve into the emerging paradigm of Graph Language Models (GraphLM) covering embedding, reasoning, and generation tasks, and discuss human-centered approaches for developing robust and ethically aligned LM systems. Additionally, the track will highlight the application of AI in environmental planning and design, emphasizing public participation and beneficial technological advancements for all citizens.
Invited Speakers (in alphabetical order)

Tyler DERR
Assistant Professor
Vanderbilt University, USA
Talk Title: Graph-Based Intelligence for Smarter Urban Futures: From Traffic Detection to AI-Driven Social Simulation

Yuxuan LIANG
Assistant Professor
Hong Kong University of Science and Technology (Guangzhou), China
Talk Title: When Spatio-Temporal Data Meet Large Language Models

Steven Jige QUAN
Associate Professor
Seoul National University, South Korea
Talk Title: Environmental Planning and Design with AI: Emerging Trends and Opportunities

Qiaoyu TAN
Assistant Professor
New York University (Shanghai), China
Talk Title: Graph Language Model: A Roadmap from Embedding, Reasoning, and Generation Tasks

Pengfei WANG
Associate Research Fellow
Computer Network Information Center, Chinese Academy of Sciences, China
Talk Title: Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction

Jie YANG
Assistant professor
Technische Universiteit Delft, Netherlands
Talk Title: Human Language Technologies: Put the Human Back in Language Technologies