About me

I'm a passionate and self-motivated PhD Candidate in Engineering at King’s College London. I dedicate to unravelling the complexities of the ever-evolving world through the lens of interdisciplinary research. My current research project delves into a dynamic intersection, where telecommunications, machine learning, and sustainability converge. As a PhD Candidate, I thrive on the challenges presented by diverse fields and find immense joy in bridging the gaps between them.

I received my BSc degree in Telecommunications Engineering with Management from Queen Mary University of London and MSc degree in Telecommunications and Internet Technology from King’s College London. My academic journey has equipped me with a versatile skill set, including programming and data analysis. As a believer in the potential of interdisciplinary collaboration, I'm excited to contribute to projects that push the boundaries of traditional academic fields.

What i'm doing

  • design icon

    Artificial Intelligence

  • Web development icon

    Machine Learning

  • mobile app icon

    Cloud Computing

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    Mathematical Optimisation

Resume

Experience

  1. Carlsberg Britvic

    Jan 2025 — Present, United Kingdom

    Data Scientist

  2. Britvic Soft Drinks

    Jul 2024 — Jan 2025, United Kingdom

    Data Scientist

Education

  1. King's College London

    Feb 2024 — Present

    Doctor of Philosophy - PhD, Engineering

  2. King's College London

    Sep 2022 — Sep 2023

    Master of Science, Telecommunications and Internet Technology

  3. Queen Mary University of London

    Sep 2018 — Jun 2022

    Bachelor of Science, Telecommunications Engineering with Management

My Languages

  • English
    Bilingual Proficiency
  • Chinese
    Bilingual Proficiency
  • Spanish
    Limited Working Proficiency

Research

  • Digital Product Passport for Manufacturing and Supply Chain

    This project develops a Digital Product Passport (DPP) system to support circular economy goals by enabling supply chain traceability, resource optimisation, and environmental impact reduction. Phase one integrates Agent-Based Modelling and Resource-Task Networks to optimise supply operations. Phase two implements edge computing for real-time data processing and decision-making. The system aims to demonstrate scalable, energy-efficient solutions for transparent, sustainable supply chains. Through case studies, it will validate the DPP’s role in fostering accountability and improving environmental and economic outcomes.

  • Life cycle optimisation tool development for process systems and centralised supply chain design

    This project focused on developing a life cycle optimisation tool for sustainable supply chain management. It uses life cycle assessment to balance profit maximisation with environmental impact reduction. The project applied both weighted sum and NSGA-II genetic algorithm approaches to solve a multi-objective optimisation problem. A UK electricity generation case study validated the model, demonstrating its ability to support net-zero decision-making while maintaining profitability.

  • Swarm intelligence for firefighting drones

    This project applied swarm intelligence to optimise drone deployment in firefighting. Inspired by social insects, drones adjusted their speed and direction based on nearby units to avoid overlap and collisions. This decentralised approach allowed more efficient area coverage compared to random movement. Python and Matplotlib were used to simulate fire scenarios and visualise drone behaviour. The results demonstrated the potential of swarm intelligence to enhance firefighting efficiency and address complex coordination challenges in real-world applications.

Contact

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