Nicola J Buttigieg

AI Research • Machine Learning • Communication

Bridging AI, Research & Communication

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Nicola is a Machine Learning/AI Engineer and Research Affiliate at the King's Institute for Artificial Intelligence. Her work focuses on building and analysing full-stack, multimodal AI systems and hybrid AI architectures, spanning retrieval-augmented generation (RAG), LangChain orchestration, multi-instrument signal processing, and containerised deployment pipelines. Currently pursuing a PhD focused on the transition from classical to quantum machine learning, her research investigates the technical and architectural constraints that shape hybrid classical–quantum workflows in real-world systems.

In 2026, Nicola presented a workload classification framework at the From AI to Quantum showcase at IBM's Innovation Centre. Designed to identify quantum-suitable computational problems and evaluate pathways toward quantum advantage, the framework explores hybrid classical–quantum architectures, feature-map encoding, variational circuit design, and hybrid optimisation strategies. The methodology provides a structured approach for assessing workload suitability across a quantum-readiness spectrum, including high-dimensional scaling and combinatorial optimisation complexity. Additionally, as part of the 2026 King’s Public Engagement Grant–funded project Quantum AI Horizons, Nicola delivered a FinTech-focused technical demonstration for industry stakeholders, executing a hybrid quantum–AI pipeline on IBM quantum hardware and comparing results against a zero-noise simulation baseline to assess performance under hardware constraints.

During her time at the institute, Nicola has served formally as an AI & Machine Learning trainer for the international King’s–Bolashak Programme, designing and delivering bespoke advanced AI training for interdisciplinary research cohorts spanning medicine, forensic science, and the technical sciences. Beyond her core research, she has an ongoing interest in developing and delivering applied machine learning prototypes using Earth observation and satellite datasets through technical seminars supporting space-sector outreach. Building on several years of work architecting AI and simulation workflows for the UKSEDS National Student Space Conference, she authored the 2025 technical series AI Software Tool Creation: Leveraging NASA MERRA-2 Data. Spanning data-centric optimisation and simulation tools, environmental forecasting, and physics-based modelling of aerospace-relevant systems, she has collaborated with NASA data specialists to present these prototypes at the NASA Goddard GES DISC Future of Giovanni forum and the NASA LaRC POWER GloCo Summit, focusing on the application of Earth observation datasets in machine learning and data analysis workflows.

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