Project management

Supervisor: Dr Shahadat Uddin

Eligibility:

• Strong knowledge of ML 
• Python (advanced)
• Analytical and statistical background            

Project Description: With recent high-tech computational advancements, AI-based systems are increasingly deployed in sensitive environments for critical and life-altering decision-making. Consequently, it is imperative to ensure that these decisions exhibit fairness and remain free from biases. 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 potentially perpetuate biases in training future algorithms. 

This research project aims to explore various aspects of fair machine learning and its ramifications across diverse domains. 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: Dr. Jin Xue; Associate Professor Petr Matous

Eligibility: 

• Have a good capability of Python, PowerBI, and SQL
• Have a basic understanding of ChatGPT or llama2
• Experience of using Langchain package is preferred
• Experience of web scrapping techniques is preferred

Project Description: As Mega Infrastructure interfaces intricately with society and public concerns, public engagement has a significant impact on Mega Infrastructure’s performance. With the rise of social media, online platforms provide an opportunity to detect public opinions dynamically in Mega Infrastructure Projects. Due to the significant feedback and widespread engagement on social media, it’s essential to identify key communities and influential figures, as it will facilitate decision-makers in understanding critical public opinions, detecting important public engagement issues, and enhancing project performance more efficiently.

This project aims to globally and nationally analyze the distribution of engaged user identities, identify "Big Influencers," and assess the sentiment influence ability of "Big Influencers" across different types of project issues. Addressing user-based analysis gaps in existing online public engagement research models, this project will utilize social network analysis (SNA) and a generative large language model (LLM) for identity and sentiment analysis of online public engaged users worldwide.

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

Supervisors: Professor Jennifer Whyte; Dr Wei-Ting Hong

Eligibility: 

• Familiar with Python, machine learning, or natural language processing 
• Skills in project management, infrastructure projects, project models, or digital delivery

Project Description: This project aims to simplify complex projects and support decision-makers across various sectors by leveraging the power of technology. Specifically, this project would like to inform project models and digital delivery by revealing patterns in project outcomes and bringing together insights on project delivery models and methods with those on systems integration and the transformative nature of digital information. The student awarded this internship will primarily investigate the possibility of applying artificial intelligence and data science to the real-world practice of digital transformation.

The student is also expected to look into any innovation applicable to project delivery models and bridge the gap between technology and project industry practice. The outputs from this internship program include one scientific paper on innovation in project digital transformation 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.