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PhD Thesis Award 2025

The Graduate School establishes the PhD Thesis Award to recognise, reward and promote the distinguished research achievements by graduating PhD students. There are two award classes, namely Outstanding and Merit, recognising students’ performance of different levels.

  • PolyU PhD Thesis Award - Outstanding Award
  • PolyU PhD Thesis Award - Merit Award


Outstanding Award


This research reveals how global warming accelerates drought development, explains why soils dry suddenly and trigger multiple climate hazards, and provides knowledge to help societies prepare and adapt.
QING Yamin_web

STEM Category 

Faculty of Construction and Environment (FCE)

Department of Land Surveying and Geo-Informatics (LSGI)

Dr QING Yamin

Thesis Title: Advancing the understanding of soil drought dynamics and mechanisms in a warming climate

- For contribution to understanding of soil drought dynamic and its critical role in multi-hazard climate extremes

Chief Supervisor: Professor WANG Shuo

Climate change is reshaping the world we live in, disrupting food systems, straining water supplies, and exposing communities to more frequent and severe natural hazards. My thesis addresses one of the most urgent challenges within this crisis: the sudden and severe droughts that endanger food security, water resources, and livelihoods. These rapidly evolving droughts can develop within days, leaving farmers, communities, and governments little time to respond. To tackle this, I developed a new and reliable method to identify such events, enabling earlier warnings that can help safeguard crops, secure water supplies, and reduce wildfire risks. My research shows that droughts are intensifying more quickly, even in regions once considered water secure, and that sudden soil drying often triggers dangerous heat waves or is followed by extreme floods when rains return. By revealing these dynamics, my work provides knowledge that helps societies prepare, protect the vulnerable, and build sustainable resilience.

My research advances the understanding of stereotype formation and language-based bias and demonstrates that discrimination or lost opportunities due to individuals’ English accents are unjust.
ABOH Sopuruchi Christian_web

Non-STEM Category

Faculty of Humanities (FH)

Department of English and Communication (ENGL)

Dr ABOH Sopuruchi Christian

Thesis Title: Accent and social evaluation: a sociolinguistic analysis of language attitudes and stereotypes in university settings in Nigeria

- For contribution to sociolinguistics and world Englishes

Chief Supervisor: Professor LADEGAARD Hans J.

Discrimination based on English accents is a pervasive issue in many societies, where speakers are often judged simply because their accent is considered ‘non-standard’ or ‘non-native’. This bias can impact access to education, employment, and social acceptance, reinforcing inequalities and undermining the value of linguistic diversity. This thesis explores how people’s attitudes towards various English accents can shape social interactions, influence stereotypes, and lead to discrimination. By examining Nigerian students’ attitudes and the effects of gender, religion, and ethnicity, the research shows how English accents can both bring people together and create divisions, sometimes resulting in stereotypes or unfair treatment based on speech. This thesis makes a significant contribution to societies worldwide by challenging the marginalisation of ‘non-native’ English accents and affirming the legitimacy of these accents. The thesis advocates more equitable language policies and practices, aiming to reduce accent-based prejudice and support sociolinguistic justice in multicultural communities worldwide.

 

Merit Award


Establishing a universal framework to manage multi-timescale dynamics in electrochemical devices for renewable energy storage.
LIANG Zhaojian_web

STEM Category

Faculty of Engineering (FENG)

Department of Mechanical Engineering (ME)

Dr LIANG Zhaojian

Thesis Title: Theoretical investigations of the transient characteristics of solid oxide electrolysis cells (SOECS) under unstable operational conditions

- For contribution to hydrogen technology and renewable energy systems

Chief Supervisor: Professor LI Mengying

To store fluctuating renewable energy like solar power, efficient and durable energy-conversion devices are essential. Our research investigated how solid oxide electrolysis cells (SOECs) react to rapid changes in power, similar to when a cloud passes over a solar panel. We found that electricity, gas, and heat within the SOEC respond at different timescales: gas adjusts within a second, while heat changes occur over minutes. By connecting the principles of fluid mechanics and electrochemistry, we derived general characteristic times that predict these dynamic behaviours. These characteristic times were validated experimentally and across various electrochemical devices in the literature, establishing a universal framework for designing safer and more efficient energy systems. With these insights, we developed a control method for a solar-powered SOEC system that uses fast gas adjustments to manage slow heat changes. This maintains safe temperatures and high efficiency, making a crucial step for reliable, large-scale renewable energy storage.

This research develops Neural RF Radiance Fields to accurately model wireless channels, providing breakthroughs in indoor localisation, 5G coverage prediction, and digital twins for next-generation communication systems.
ZHAO Xiaopeng_web

STEM Category

Faculty of Computer and Mathematical Sciences (FCMS)

Department of Computing (COMP)

Dr ZHAO Xiaopeng

Thesis Title: Neural radio-frequency radiance fields: development and applications

- For contribution to AI‑driven wireless channel modeling and spatial intelligence sensing technologies

Chief Supervisor: Professor YANG Lei

My doctoral research tackles a very practical challenge: how radio signals, like Wi-Fi and 5G, actually travel through the real world. In everyday life, these signals bounce, scatter, and weaken when they pass through walls or buildings, which makes it hard to get accurate indoor navigation, smooth video calls, or strong wireless coverage everywhere. To address this, I developed a new way of modelling radio waves called Neural RF Radiance Fields. This approach combines physics with artificial intelligence to predict how signals behave in complex environments. The results are already making a difference: centimetre-level indoor positioning that could guide people in airports or shopping malls, smarter placement of Wi-Fi and 5G antennas for faster speeds, and more efficient use of wireless networks in crowded cities. Supported by Huawei and Hong Kong’s innovation funds, this work bridges science and society, helping build more reliable, accessible, and intelligent wireless services for everyone.

Leveraging multi-source internet big data and advanced forecasting models to better predict tourism demand, support crisis management, and promote sustainable development in the ever-changing tourism industry.
GAO Huicai_web2

Non-STEM Category

School of Hotel and Tourism Management (SHTM)

Dr GAO Huicai

Thesis Title: Forecasting tourism demand using big data

- For contribution to methodological advancement and practical application in tourism demand forecasting through artificial intelligence and big data analytics

Chief Supervisor: Professor LI Neil

This thesis develops innovative methods to generate more accurate tourism demand forecasts, offering both academic value and practical benefits for society and the tourism industry. By integrating diverse Internet big data sources such as search queries and online reviews, the research demonstrates how digital footprints can be transformed into reliable indicators of travel patterns and visitor intentions. These enhanced forecasts enable tourism businesses, governments, and destination managers to make better-informed decisions regarding resource allocation, marketing strategies, and crisis response. Methodologically, the study advances forecasting techniques by introducing fine-grained sentiment analysis and dynamic weighting methods that improve prediction accuracy and robustness. Notably, the findings highlight the digital resilience of Internet big data in supporting sustainable and adaptive tourism management during uncertain times.

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