About Me

I am currently an M.S. student in Computer Technology at Shenzhen University. Before that, I received my B.S. in Information and Computational Science from Guangdong University of Technology. My research develops intelligent systems for complex physical and spatial environments through an AI-driven interdisciplinary approach.

My current work centers on AI for Agri-Climate Systems, bridging physics-based modeling, data-driven learning, and agent-based reasoning to support reliable decisions in dynamic environmental systems. I am especially interested in physics-informed operator learning, knowledge-enhanced deep reinforcement learning, constrained spatial decision-making, and generalizable knowledge-aware agents for safety-critical scientific applications.

Research Interests

• Physics-Informed Environment Modeling — learning multi-scale representations for complex spatial systems, including spherical modeling for meteorological downscaling under strong physical priors.
• Knowledge-Integrated Spatial Decision-Making — integrating domain knowledge, feasibility constraints, and learning-based optimization for spatial planning and strategy optimization.
• Knowledge-Aware Scientific Agents — building interpretable world models and foundation-model-enabled agents with episodic memory, causal reasoning, and safety-aware multi-agent coordination.
• AI for Climate Adaptation and Environmental Management — translating AI methods into trustworthy long-horizon decision support for agri-climate systems.

My long-term vision is to advance generalizable, knowledge-aware agent systems at the intersection of AI and multidisciplinary science, enabling trustworthy long-horizon decisions in safety-critical domains such as climate adaptation and environmental management.

🔥 News

• 2026.4.28: MSPNO was accepted by IJCAI 2026. It learns high-resolution meteorological fields with spherical, multi-scale, and physics-informed neural operators.

📖 Education

• 2023 - 2026 | M.S. in Computer Technology
  Shenzhen University, Shenzhen, China
  Research on physics-informed operator learning and knowledge-enhanced decision optimization.
• 2019 - 2023 | B.S. in Information and Computational Science
  Guangdong University of Technology, Guangzhou, China
  Recommended for Admission.

💻 Research

MSPNO: Spherical Physics-informed Neural Operator for Meteorological Downscaling
A spherical, multi-scale, and physics-informed operator learning framework for high-resolution climate field modeling. The project combines spherical Laplacian decomposition, localized spherical integral operators, and physics-informed constraints to improve geometric continuity and physical consistency.

PTO: Precise Technology Optimization for Agricultural Mitigation
A county-level technology portfolio optimization pipeline covering 2,901 counties, 105 mitigation technologies, 18 carbon and nitrogen indicators, 4,538 publications, and 22,635 observations. PTO combines expert-guided initialization and curriculum learning to reduce emissions, cost, and implementation complexity.

Knowledge-Enhanced DRL for Livestock Spatial Planning
A constrained actor-critic planning framework for livestock redistribution under agronomic, environmental, and social constraints. The framework uses PPO, action masking, hierarchical migration/intervention stages, and DRL+LP optimization. Scenario results report 63% nitrogen surplus reduction, 48% ammonia-emission-intensity reduction, and 71% pollution-exposure reduction.

📝 Selected Publications

* denotes equal contribution or co-first authorship where indicated by the source CV. Status labels are reported conservatively.

• Spherical Physics-informed Neural Operator with Multi-scale Coupling for Meteorological Downscaling
  Yiqiang Ye^*, Yichi Wang^*, Jiawei Wen, Jiahui Jiang, Zhaoyu Zhong, Jiangjian Yu, Chunxia Xiao, Haodi Zhang.
  Accepted, IJCAI 2026, CCF-A. Co-first author.

• Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations
  Jiahong Li, Yiyuan Chen, Yichi Wang, Yiqiang Ye, Min Sun, Hao Ren, Weibin Cheng, Haodi Zhang.
  Published, SMC-IoT 2023, EI.

🏆 Honors & Awards

• 2024–2025: Outstanding Student Scholarship, Second Prize, Shenzhen University.
• 2023: Grand Prize Student Scholarship, Shenzhen University.
• 2019–2023: Outstanding Student Scholarship, Third Prize, Guangdong University of Technology.

📬 Contact

I am open to research discussions on AI4Science, physics-informed operator learning, constrained reinforcement learning, and knowledge-aware scientific agents.

• Email: yaniszz085@gmail.com
• CV: Yiqiang-Ye-CV-EN.pdf