Artificial Intelligence & Risk (AIR) Laboratory

Led by Prof Jize Zhang, the Artificial Intelligence & Risk Laboratory at Hong Kong University of Science and Technology leverages cutting-edge AI and computational science to tackle pressing real-world problems.

Our work spans developing reliable, efficient AI foundation model-driven methods for robust infrastructure defect detection from drone imagery, to creating scalable, GPU-accelerated neural models for rapid and accurate natural hazard modeling.

Join us: We always welcome applications from talented researchers. Please refer to Apply.

Our work

Infrastructure Defect Detection with AI and Foundational Models

Neural-parameterized, GPU-accelerated Tropical Cyclone and Storm Surge Modeling

Interaction between Uncertainty Quantification and AI

Physics-informed Machine Learning for Wind and Wave Energy

Our news

24-Aug-2026: Xi defended her PhD thesis. (more)

01-Jul-2026: Prof Zhang invited to the Editorial Board of Communications AI & Computing. (more)

26-Jun-2026: RGC General Research Fund on operational storm surge forecasting awarded to Professor Zhang. (more)

27-May-2026: Han defended his PhD thesis. (more)

15-Apr-2026: Chen defended his PhD thesis. (more)

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Our priorities

Low Altitude Economy

Control, sensing, and decision-making for UAVs.

Uncertainty Quantification

UQ for large-scale AI models and engineering solvers.

Natural Hazard Modeling

Scalable and efficient models for natural hazards.

Our selected publications

2026

Accurate concrete spalling segmentation from bounding box supervision using Segment Anything (2026) · Chen Zhang, Dhanada K. Mishra, …, Yantao Yu, Jize Zhang · Automation in Construction · Cited by 1

2025

SelectSeg: Uncertainty-based selective training and prediction for accurate crack segmentation under limited data and noisy annotations (2025) · Chen Zhang, Mahdi Bahrami, …, Yantao Yu, Jize Zhang · Reliability Engineering & System Safety · Cited by 29

Efficient, scalable emulation of stochastic simulators: A mixture density network based surrogate modeling framework (2025) · Han Peng, Jize Zhang · Reliability Engineering & System Safety · Cited by 28

2024

Surge-NF: Neural Fields inspired peak storm surge surrogate modeling with multi-task learning and positional encoding (2024) · Wenjun Jiang, Xi Zhong, Jize Zhang · Coastal Engineering · Cited by 36

TC-SINDy: Improving physics-based deterministic tropical cyclone track and intensity model via data-driven sparse identification of Nonlinear Dynamics (2024) · Xi Zhong, Wenjun Jiang, Jize Zhang · Journal of Wind Engineering and Industrial Aerodynamics · Cited by 16

All publications

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