👋 Hi, I’m Jiang Yue

Hello! My name is Jiang Yue. I am currently a PhD student at Nanyang Technological University (NTU) under the Alibaba-NTU Joint Talent Programme supervised by Prof. Cong Gao. I received my Bachelor’s degree from NTU and my Master’s degree from the National University of Singapore (NUS). My research focuses on spatial-temporal data mining, artificial intelligence, and machine learning. I am interested in developing advanced machine learning models to tackle practical challenges such as spatio-temporal data analysis, spatio-temporal LLMs and foundation models, AI-driven decision-making, and the application of machine learning techniques in various domains.

📧 Email: yue013@e.ntu.edu.sg 📱 Weixin:

Education

• Ph.D. in Computer Science, Nanyang Technological University (NTU), Aug 2022 – Present
  College of Computing and Data Science (CCDS)

• M.Eng. in Mechanical Engineering, National University of Singapore (NUS), Jan 2020 – Jan 2022

• B.Eng. in Aerospace Engineering, Nanyang Technological University (NTU), Aug 2013 – May 2017
  First Class Honours · NTU President Scholar (2014–2015) · Dean’s List (Top 5%) (2015–2016) · Senior Middle School Scholarship (MOE, 2012–2017)

Work experience

• Aug 2022 – Present: Researcher (PhD Candidate)
  Alibaba Cloud (Singapore) / NTU
  • Research on data + AI: spatio-temporal learning, time-series forecasting, LLMs/foundation models for urban computing.
  • Intelligent data systems and optimization with LLMs.
• Jun 2021 – Jul 2022: Research Associate
  Nanyang Technological University (NTU)
  • Collaborated with NCS & LTA on an AI-enabled smart car-park system.
  • Collaborated with ST Engineering on an AI-enabled trajectory analytics project.
• Jul 2017 – May 2021: Senior/Design Engineer
  ASM Pacific Technology
  • Mechanical system design for industrial material-handling.
  • Drove process improvements and operational efficiency.
• Dec 2015 – Jul 2016: 3D Printing Engineer Intern
  SIMTECH, ASTAR*
  • Additive manufacturing for high-precision engineering; metallic 3D printing for bone replacement applications.

Publications

Next-LocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs.

Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization.

FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting.

Self-supervised Learning for Geospatial AI: A Survey.

Disentangling Dynamics: Advanced, Scalable and Explainable Imputation for Multivariate Time Series.

UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models.

SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting.

Multivariate time-series imputation with disentangled temporal representations.

Electron Beam Melted Heterogeneously Porous Microlattices for Metallic Bone Applications: Design and Investigations of Boundary and Edge Effects.

Skills

• Python (PyTorch, Hugging Face, LLM inference & QLoRA/LoRA fine-tuning, data processing)
• Spatio-Temporal ML (time-series forecasting, STGNNs, retrieval-augmented TS, Urban/transport data)
• Tools: Git/GitHub, JAX/NumPy/Pandas, Docker, Linux
• Languages: Mandarin, English

Service

• Reviewer: TKDE 2024–2025, KDD 2025, EMNLP 2024
• External Reviewer: NeurIPS 2025, SIGSPATIAL 2025, VLDB 2023, CIKM 2023/2025