Project management

Supervisor: Dr Xinyue Zhang

Eligibility: Candidates with experience in qualitative case studies will be given priority. Applicants with a keen interest in case study research are also encouraged to apply

Project Description:

The dual nature of major infrastructure projects as both public goods and commodities necessitates a unique approach to their governance. These projects are critical for providing fundamental public services and supporting societal and economic development. Simultaneously, they operate within a market-driven environment where resource allocation and stakeholder interactions are guided by market principles.

This duality poses significant challenges for project governance, which must extend beyond traditional project management practices to encompass broader governance domains influenced by both government and market forces.

This project plans to explore the governance model of major infrastructure projects, investigating the combined influence of government and market forces. The research will adopt multiple case studies, examining some prominent projects worldwide.

The findings are expected to reveal the intricate dynamics of government and market participation across different organisational levels and phases of major infrastructure projects. The successful candidate will conduct data collection and analysis of case materials.

Requirement to be on campus: No

Supervisor: Dr Xinyue Zhang

Eligibility: Proficiency in reading and writing Chinese characters to analyse raw data from case materials.  Weighted Average Mark≥80.

Project Description:
Inter-project learning is crucial for improving future project management as it allows organisations to transfer valuable insights and experiences from completed projects to new ones.

Projects are often seen as one-off and unique, making inter-project learning seem challenging. However, as projects are embedded in permanent organisations (such as owners, contractors, construction firms) means that the experiences from previous projects can be integrated into these permanent organisations and then passed on to the next project, enabling inter-project learning.

This project plan is based on the case study from Beijing Capital Airport T3 project to Beijing Daxing International Airport project, investigating the mechanisms of inter-project learning. The research aims to reveal how past project experiences can be utilised to improve future projects. The successful candidate will analyse the already collected raw data to explore the mechanisms of inter-project learning.

Requirement to be on campus: Yes *dependent on government’s health advice

Supervisor: Dr Shahadat Uddin

Eligibility:

• Strong knowledge of ML
• Python (advanced)
• Analytical and statistical background
• Entirely on-campus

Project Description:

With recent high-tech computational advancements, AI-based systems are increasingly deployed in sensitive environments for critical decision-making. The project management domain has also recently experienced an extensive adoption of AI-based tools and technologies for decision-making. Consequently, ensuring that these decisions exhibit fairness and remain free from biases is imperative.

A growing body of literature highlights instances where real-world AI systems generate unfair and biased outcomes, often attributed to the data used, underlying algorithms, and user interactions. Beyond causing false alarms within specific contextual settings, these inequitable results can perpetuate biases in training future algorithms.

This research project aims to explore various aspects of fair machine learning and its ramifications across diverse domains, emphasising project analytics. By developing approaches to detect and mitigate bias, the project seeks to prevent the occurrence of unfair AI-based decisions, fostering a more equitable and just application of artificial intelligence.

Requirement to be on campus: Yes, *dependent on government’s health advice.

Supervisors: Prof. Jennifer Whyte, Dr Wei-Ting Hong

Eligibility: Preferred:

• Familiar with natural language processing, prompt engineering, large language models, and Python
• Skills in project management, infrastructure projects, project models, or digital transformation

Project Description:

This project investigates how to leverage emerging technologies and data-driven approaches to support complex project delivery and enhance the efficiency of information exchange across various sectors. Specifically, this project aims to standardise data from different stakeholders in a complex project, such as construction drawings and incident reports, and build a prototype of a decision-making support system. This system is expected to enhance the efficiency of project delivery tasks, including scheduling, cost estimating and risk management.

The awarded student will explore the challenges in real-world practice in complex projects and design solutions using artificial intelligence and data science. The student is also expected to bridge the gap between technology and project industry practice by engaging with industry partners. The outputs from this internship program include one scientific paper on enabling AI in project decision-making and a presentation detailing the findings and implications for future project delivery models.

Requirement to be on campus: Yes, *dependent on government’s health advice.

Supervisor: Prof. Lynn Crawford

Eligibility: Credit WAM or above.  Demonstrated capability in English language and critical thinking

Project Description:

The aim of this project is to undertake a systematic review of research to identify key factors that impact sustainable knowledge worker productivity. The review will analyse existing literature, focusing on both internal and external factors, including knowledge management practices, organisational culture, self-management skills, job satisfaction, role clarity, autonomy, sustainable work practices, and communication. 

By systematically synthesizing findings from diverse studies, the project aims to highlight critical drivers of sustainable knowledge worker productivity. The outcome will be a comprehensive report that documents the process of the review and outlines critical factors that have been identified by research as affecting sustainable knowledge worker productivity. 

This review will serve as a valuable resource for human resource Professionals, project managers, and policymakers seeking to optimise workforce performance and achieve sustainable organisational growth. You will learn how to conduct a systematic review and how to use software and AI to assist in qualitative analysis.

Requirement to be on campus: No

Supervisor: Prof. Lynn Crawford

Eligibility: Credit WAM or above.  Demonstrated capability in English language and critical thinking

Project Description:

The aim of this project is to conduct a systematic review of research identifying key factors that influence knowledge worker wellbeing in the workplace. The review will synthesize existing studies, focusing on positive, negative, and mediating factors of employee wellbeing, including but not limited to work environment, emotional intelligence, perceived stress, work engagement, and transformational leadership.

By evaluating the breadth of empirical evidence, the project seeks to identify factors, identified by research, that positively or negatively affect or influence knowledge worker wellbeing. The outcome will be a comprehensive report that documents the process of the review and outlines critical factors that have been identified by research as affecting workplace wellbeing.  You will learn how to conduct a systematic review and how to use software and AI to assist in qualitative analysis.

Requirement to be on campus: No

Supervisors: Prof. Lynn Crawford

Eligibility: Credit WAM or above.  Demonstrated capability in English language and critical thinking

Project Description:

The aim of this project is to conduct a systematic review of research focused on identifying factors that influence sustainable team productivity in the workplace. The review will examine a wide range of studies to understand the role of team dynamics, communication, leadership styles, collaboration tools, and organizational culture in shaping team performance.

By synthesising findings from diverse research, the project will uncover critical factors that enhance or hinder team productivity, highlighting best practices and common challenges. The final report will offer evidence-based recommendations for improving team efficiency, fostering effective collaboration, and optimizing team outcomes.

The outcome will be a comprehensive report that documents the process of the review and outlines critical factors that have been identified by research as affecting sustainable team productivity in the workplace.  You will learn how to conduct a systematic review and how to use software and AI to assist in qualitative analysis.

Requirement to be on campus: No

Supervisors: Dr Sujuan Zhang, Dr Louis Taborda

Eligibility:

• WAM above 80
• Passion for knowing, eager to learn
• Basic knowledge about sustainable infrastructure
• Basic skills of doing literature review, case study

Project Description:

Drawing on the grand challenges of sustainable development and climate resilience, there is a great necessity of achieving sustainable infrastructure development. This current study aims to incorporate the idea of Environmental, Social, and Governance (ESG) to propose a project-level framework that is suitable to evaluate sustainable infrastructure at the shaping, delivery and operation stages.

Mixed methods will be used. We will firstly establish a project-level ESG framework for sustainable infrastructure, then collect cases about major infrastructure projects, and see how managers can rely on the framework to improve ESG performance in different contexts. From the cases, we can then look into how stakeholders (e.g. owner organizations) transform their practice to incorporate ESG principles.

The study aims to add theoretical insights to the evolution of project governance for infrastructure investment, delivery, and operations and contributes to the achievement of ESG performance of infrastructure projects in practice.

Requirement to be on campus: Yes, *dependent on government’s health advice.

Supervisors: Dr Hoonyong Lee, Dr Louis Taborda

Eligibility:

• Experience with Time-series Data Analysis;
• Proficiency in Signal Processing;
• Knowledge of Deep Learning

Project Description:

Effective stress assessment is crucial for enhancing team cohesion. To monitor individual stress levels, wearable electrodermal activity (EDA) sensors are commonly used by measuring changes in skin conductance caused by physiological arousal.

While previous studies have focused on anomaly detection in EDA data, current approaches often rely on prior knowledge (e.g., predetermined EDA patterns or pre-trained models), limiting their reliability across different individuals, contexts, and stress triggers.

To address these limitations, this research aims to develop an alternative approach for assessing individual stress levels without relying on prior knowledge. During the summer research internship program, students will conduct preliminary tests to measure individuals’ stress levels in various environments using EDA sensors and develop an unsupervised algorithm to detect abnormal patterns in the collected data.

The successful completion of this research will provide students with valuable opportunities to bridge theoretical knowledge with practical applications and broaden their perspectives across diverse fields.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisors: Dr Jin Xue, A Prof. Petr Matous

Eligibility:

• Have a good capability of Python, PowerBI, and SQL
• Have a basic understanding of ChatGPT or llama3
• Experience of using Langchain package is preferred
• Experience of web scrapping techniques is preferred

Project Description:

The success of mega-infrastructure projects (MIPs) depends on stakeholder coordination and collaboration.

Capturing the dynamics of stakeholder networks in MIPs is challenging in terms of data collection, coding , analysis, and visualisation.

Building on recent developments in novel data science techniques (Large Language Models, big data analytics, network analysis), we will develop a systematic data-driven method to construct dynamic stakeholder networks in the lifecycle of MIP.

The project will leverage data from stakeholder reports, social media and public databases, to automatically identify key stakeholders, capture their attributes and their relationships in MIPs.  

Constructing and assessing longitudinal stakeholder networks as these change through MIP lifecycle, and pinpointing critical actors in MIP evolution, the project will inform and advance the application of AI and big data analytics for effective project management.

Requirement to be on campus: Yes, *dependent on government’s health advice.

Supervisor: Dr Neda Mohammadi

Eligibility:

• Bachelor's degree in related field (e.g. engineering, computer science, or related disciplines; minors in AI, analytics, data science, or computational science are a plus);
• Background in quantitative research methods, mathematical modeling, data analytics, machine learning, and programming skills (e.g., Python, R, MATLAB);
• Eagerness to work in a multidisciplinary, multi-cultural team

Project Description:

This research project focuses on the role of Digital Twins as cyberinfrastructure-enabled tools for engineering and technology management, aimed at enhancing infrastructure sustainability. By creating digital replicas of physical infrastructure, the project will explore how Digital Twins can be applied for real-time monitoring, predictive analysis, and decision-making within complex environments.

The research will also emphasize the importance of effective engineering to project and technology management in the implementation and scaling of these technologies, ensuring they contribute meaningfully to the United Nations Sustainable Development Goals (SDGs).

Requirement to be on campus: Yes, *dependent on government’s health advice.