Qingfeng Zhang is a Ph.D. candidate at Wuhan University under the supervision of Prof. Jiyun Song, having ranked first in his B.Eng. at Northwest A&F University and been exempted from exams for direct doctoral admission, with his bachelor's thesis selected as a Top 100 Distinguished Paper. His research integrates urban climate dynamics, land-atmosphere interactions, and physics-informed spatiotemporal deep learning to address heat extremes and climate-just urban resilience, developing causal machine learning frameworks to decouple urbanization-induced thermal effects across 1,690 Chinese cities, a physics-guided deep learning system for 5-meter urban thermal stress mapping (reducing heat stress by 21.1%), and a physics-informed STGNN for station-to-grid weather nowcasting that reduces WRF biases by 25–67%—with corresponding manuscripts under review at Science Advances, npj Urban Sustainability, and Atmospheric Chemistry and Physics. He has delivered numerous oral presentations at EGU, AOGS, ICUC12, and other international forums, and has won over a dozen national and provincial awards, including grand prizes in water resources and robotics competitions. He is proficient in WRF-UCM, HPC, PyTorch-based deep learning, causal inference, and geospatial analysis.

Deng Zhang became a Ph.D. student in Healthy Cities Lab at the School of Water Resources and Hydropower Engineering, Wuhan University since September 2024. His current research interests include fine-resolution modelling of urban microclimate and carbon emission by integrating machine learning and physical urban climate models. Prior to this role, he obtained his bachelor's degree in Hydrology and Water Resources Engineering at Northwest Agriculture and Forestry University (GPA ranking: 2/57). During his undergraduate years, he has been awarded seven scholarships, including the China National Scholarship (once) and National Motivational Scholarship (twice).

Jinhao Zhang is a direct-entry Ph.D. candidate in the School of Water Resources and Hydropower Engineering (affiliated with the Water-Carbon Cycles and Carbon Neutrality Pilot Program) at Wuhan University (WHU) supervised by Prof. Jiyun Song. He completed his B.Eng. at WHU with an Outstanding Undergraduate Graduation Thesis Award and multiple university honors for leadership and volunteerism. His research interests encompass urban hydro-meteorological processes, land-atmosphere interactions, physics-driven microclimate simulation, urban canopy model (UCM) development, and multi-objective optimization for resilient low-carbon cities. He has delivered oral and poster presentations at major international conferences such as EGU, AOGS, ICUC12, and the China Water Forum and has undertaken academic exchanges at The University of Hong Kong and the National University of Singapore. He is proficient in UCM, MATLAB, Python, GAMS, ArcGIS, and Revit.

Zhangchi Zhou joined Healthy Cities Lab as an MPhil student at Wuhan University (WHU) since 2025. He obtained his B.Eng. degree in School of Water Resources and Hydropower Engineering (affiliated with the Water-Carbon Cycles and Carbon Neutrality Pilot Program) at WHU, graduating as an Outstanding Graduate, and has been granted direct admission to an academic master's program with a high GPA (ranked Top 4 in his class). His current research interests focus on two areas: (1) Urban Land–Atmosphere Interaction: Investigating the seasonal and diurnal variations of urban energy fluxes and CO2 fluxes via eddy covariance measurements and wind lidar; (2) AI + Urban Hydro Meteorology: Integrating fixed flux tower and mobile flux observations with machine learning methods to achieve high spatiotemporal mapping of urban evapotranspiration.

Longqing Zhao became a Ph.D. student at the Healthy Cities Laboratory, School of Hydraulic and Hydropower Engineering, Wuhan University since 2025. He received his master's degree from Wuhan University in 2025. His research interests include (1) high spatiotemporal resolution urban rainfall nowcasting. (2) urban inundation risk analysis.(3) Flooding Risks in Wuhan City under Future Land Use Change Scenarios.His work aims to enhance the accuracy and lead-time of extreme-event forecasts, bridging the gap between advanced AI methodologies and operational flood early‑warning systems. He has developed systematic benchmarking frameworks to evaluate cutting‑edge forecasting models and has designed targeted optimisation strategies to improve performance on heavy‑rain cases. He actively disseminates his findings at top international forums, having presented at the EGU General Assembly 2026 and the CAWA conference, and he has engaged in academic exchanges with the University of Hong Kong.

Ruiqi Peng is a Ph.D. student at Wuhan University’s School of Water Resources and Hydropower Engineering, having completed his B.Eng. in the Carbon Neutrality Pilot Class with a strong GPA of 3.78/4.0 under the supervision of Prof. Song. His honors include a National Encouragement Scholarship and multiple university awards, along with a provincial prize in mathematical modeling. His research focuses on urban water–heat–carbon coupling mechanisms, integrating the WRF (Weather Research and Forecasting) model with spatiotemporal data analysis to investigate multi-element interactions at the city scale. During his undergraduate Carbon Neutrality Pilot Program at WHU, he has developed solid skills in numerical modeling, geospatial analysis, and scientific programming (Python, MATLAB), and gained hands-on experiences through field measurement campaigns and academic exchange visits to the University of Hong Kong. He is motivated to apply physics-informed modeling to inform carbon-neutral urban planning and is always eager to take on new challenges in upcoming research.

Kaiyu Zhang is a highly motivated Ph.D. student (starting from Sep 2026) at Wuhan University’s School of Water Resources and Hydropower Engineering under the supervision of Prof. Song, having completed his B.Eng. in the same school with a minor in Computer Science, maintaining GPAs above 3.5/4.0. His research interests lie at the intersection of mobile mapping, urban meteorology, carbon emissions, and spatiotemporal machine learning, as demonstrated by his undergraduate thesis on high-resolution road CO₂ mapping in Wuhan using ensemble models and SHAP/PDP analysis, and his minor thesis on a full-stack supervision log system integrating RAG-based voice dictation. He has also contributed to professional projects, including an automated Civil 3D plugin and an intelligent supervision system, and earned national and university-level competition awards in water conservancy and flood monitoring. Proficient in multiple programming languages (Python, Java, C++, etc.), IoT platforms, and software tools, with English proficiency at IELTS 6.0, he combines strong technical skills with applied environmental engineering research.

Tiantao Jia joined Healthy Cities Lab as a master’s student majoring in Hydrology and Water Resources at Wuhan University under the supervision of Prof. Song. He earned his Bachelor's degree in Soil and Water Conservation from Northwest A&F University with an outstanding GPA of 3.71/4.0 and ranking first among 65 students. and was admitted to Wuhan University through postgraduate recommendation. He has been recognized with the National Scholarship (twice), first-class professional scholarships, and over ten provincial or national awards in mathematics, mathematical modeling, AI innovation, and programming competitions. He is proficient in ArcGIS, SPSS, Origin, Python, and CAD, demonstrating a solid foundation for interdisciplinary environmental research. His research focuses on the impacts of urbanization on regional climate, as well as the characteristics and associated environmental and social risks of compound extreme weather events. He hopes to deepen relevant theoretical research and provide scientific support for sustainable urban development.

Xingming Huang is currently an M.S. student (starting from Sep 2026) in Hydrology and Water Resources at Wuhan University supervised Prof. Jiyun Song, having earned his B.Eng. in the same field from Hohai University with a thesis awarded as University‑level Excellent Undergraduate Thesis. His research interests focus on multi‑scale urban meteorological modeling, high‑resolution weather simulation, urban climate extremes, and machine‑learning‑based fusion and downscaling of multi‑source remote sensing data, exemplified by his undergraduate project using Random Forest and LSTM to improve soil moisture spatial resolution over Anhui Province. He has actively participated in competitions, winning prizes in provincial and national mathematics contests, a water model design competition, and leading an innovation program on satellite soil moisture fusion. He also attended Peking University’s Summer School on Intelligent Watershed Management, gaining training in IWS and CWatM. He achieved a strong graduate entrance exam score of 396, and is proficient in MATLAB, Python, hydrological analysis tools, and machine learning frameworks.