Miss Tia Haddad

Research project: A privacy-preserving microservices architecture to support epidemiologic research using large health datasets


An epidemiology research project's success depends on the efficiency of its data processing workflow and its ability to create value. It is often necessary to harmonise data from multiple sources, discover patterns and efficiently compute useful projections. There is a significant gap between the potential of these datasets and their actual use, despite the advancements in the software engineering domain. Considering the developments in the Microservices architecture (MSA), the Cloud computing paradigm and Artificial Intelligence tools, more scalable, cost-effective, and distributed processing approaches can be explored for healthcare data processing. This research is aimed at developing an innovative healthcare data processing architecture by utilising microservices-based, hybrid-cloud-hosted collaborative data processing pipelines to improve scalability, efficiency, and privacy-preserving characteristics. As case studies prominent epidemiological data where warehouse systems' workflow will be studied and modelled. We then wish to explore the viability of multiple microservices data processing pipeline designs to replicate the existing workflows, introduce process improvements and process reengineering, with further developments focusing on expanding the proposed architecture into a hybrid-cloud environment, adding in-memory processing capabilities and AI features for autonomous load balancing. This study will yield architecture designs and potential data processing frameworks that will be beneficial to multiple entities involved in healthcare networks.


I graduated from Kingston University London with a First-class degree in BSc Computer Science (Hons). My performance and outstanding achievement encouraged me to pursue a PhD research in Software Engineering, supervised by Dr Pushpa Kumarapeli. My research interests lie in exploring cloud computing paradigms, Machine Learning approaches, Artificial Intelligence tools, and more scalable distributed processing approaches for efficient healthcare data processing. During my time at the university, I sought internship opportunities to contribute to my field and enhance my skillset. For instance, I developed a mobile application that allows users to control the movement of a physical lamp while recognising various LED lights via Bluetooth Low Energy (BLE) technology, and built an Arduino device that broadcasts a BLE connection with the application. Moreover, I conducted in-depth research on Seeso's AI eye-tracking SDK, focusing on data gathered from AI eye scan calibration and detection.

Areas of research interest

  • Microservices data processing architecture
  • Epidemiologic research
  • Cloud computing
  • Machine Learning
  • Artificial Intelligence


  • BSc Computer Science (Hons), Kingston University London

Funding or awards received

  • MPhil/PhD Studentship in the Faculty of Engineering, Computing and the Environment
  • School Prize - Awarded for the best all-round performance within the school