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Postdoctoral Fellow · HKUST

Han Wang

Building scientific machine learning tools for climate and atmospheric science. 🌍🌎🌏

Atmosphere Wind Power Scientific ML
Han Wang portrait illustration

About

Current Role: Postdoctoral Researcher, HKUST Education: Ph.D. HKUST (2025) · BSc HHU (2021) Core Focus: Scientific machine learning

Hi, I'm Han! 👋

I am a Postdoctoral Researcher (CORE Fellowship awardee) at HKUST, advised by Prof. Hui Su. Before that, I completed my Ph.D. at HKUST under the supervision of Prof. Jiachuan Yang. My work sits at the intersection of AI and Earth Science, where I build scientific machine learning models for complex environmental systems. Building on my Ph.D. foundation in urban climate prediction, I am now bringing spatiotemporal modeling and Graph Neural Networks (GNNs) into wind energy and renewable energy forecasting.

I am deeply committed to the idea that AI will redefine how we conduct science and interact with our world. A lover of the great outdoors, I spend my free time hiking ⛰️, photography 📷, and spending time by the sea and scuba diving 🤿.

Research Focus

Scientific ML

Turning physics-aware deep learning into practical forecasting tools under sparse observations, domain shifts, and real-world uncertainty.

Urban Climate 🏙️

Tracing how urban form, vegetation, and synoptic forcing shape canopy-layer temperature dynamics, the physical foundation of my earlier work.

Wind and Renewable Energy

Bringing those spatiotemporal ideas into wind power and broader renewable forecasting, with an eye toward cleaner and more reliable energy systems.

Selected Works

  1. Wang, H., Chen, Y., & Yang, J. (2026). Leveraging atmospheric information across scales for local temperature forecasting using a novel multi-scale perception network. Journal of Geophysical Research: Machine Learning and Computation, 3, e2025JH001083.
  2. Wang, H., Tang, J., Zhang, J., & Yang, J. (2026). Intra-city scale graph neural networks enhance short-term air temperature forecasting. Atmospheric Chemistry and Physics, 26(2), 947-961.
  3. Wang, H., Zhang, J., & Yang, J. (2024). Time-series forecasting of pedestrian-level urban air temperature by LSTM: Guidance for practitioners. Urban Climate, 56, 102063.
  4. Wang, H., Yang, J., Chen, G., Ren, C., & Zhang, J. (2023). Machine learning applications on air temperature prediction in the urban canopy layer: A critical review of 2011-2022. Urban Climate, 49, 101499.

Referee Service

Reviewer for: Geophysical Research Letters, Environmental Research Letters, Urban Climate, Sustainable Cities and Society, Advances in Atmospheric Sciences, and Meteorological Applications.

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