Since September 2022, I have worked as a Graduate Teaching Assistant at the University of Sheffield, contributing to teaching across the School of Geography and Planning and the School of Architecture and Landscape. I am recognised as an Associate Fellow of the Higher Education Academy (AFHEA) and am currently pursuing Fellowship status (FHEA), with the outcome expected in May 2025.
ARC605 (Doctoral Training, 2022-23, ~20 students, PhD level): Designed and delivered a specialist session to support the new PhD cohort through the confirmation review process.
TRP6405 (Integrated Project, 2022–23, 200 master’s students): Led seminars for ~50 students, offering foundational knowledge and problem-solving support, and marked 80 assessments.
Urban Design and Planning Modules: Supported module leaders with content delivery, group discussions, and student queries, while also teaching software tools like SketchUp and Adobe:
Spatial Analysis and GIS Modules: Assisted in transitioning software tools (ArcMap → QGIS → ArcGIS Pro) and supported students with spatial data analysis and visualization, developed workshop handbooks, and contributed to assessment marking.
Additionally, I co-led outreach events such as the Access Year 11 event (~40 students) and the Sutton Trust Summer School (~60 students) for high school participants. I have also conducted one-to-one tutoring sessions, guiding students in software use and research techniques.
Responsibilities include:
Thesis titled “Effects of Urban Vegetation on Ambient PM2.5 Concentrations in Real Urban Environments”.
This research investigates the influence of urban vegetation on PM2.5 concentrations across different urban form types. It comprises three main components: (1) ultra-high-resolution, city-scale PM2.5 prediction; (2) urban form clustering and classification; and (3) analysis of the relationship between vegetation characteristics and PM2.5 levels. A novel Multi-view Urban Vegetation Index (MvUVI) was developed to quantify vegetation spatial coverage from multiple perspectives. This is a data-driven quantitative study involving machine learning, deep learning, and GIS-based spatial analysis.
GPA: 83.15/100
Thesis title “Analyzing the Impact of the Green Interface Index on PM2.5 Concentrations in Typical Street Canyons of Harbin”.
This study investigates the relationship between the green interface index (e.g., Leaf Area Index, Leaf Area Density, plant spacing) and PM2.5 concentrations in typical street canyons in Harbin. Key factors such as wind speed, wind direction, and street canyon orientation were considered. The research combines field measurements with numerical simulations using Computational Fluid Dynamics (CFD).
GPA: 87.09/100 (Rank: Top 5%)
Courses included: