Evidence highlights

Roche / GOSH applied AI leadership

Led multiple workstreams in a regulated paediatric environment, aligning clinicians, technical teams, and decision-makers.

Genomics NLP pipeline

Built an end-to-end PDF-to-FHIR pathway and clinician dashboard, with an 80% reduction in manual review time.

Clinical ML with interpretability

Co-led CKD progression modelling (n=692), with XGBoost as best model (F1 0.72, ROC AUC 0.80) and SHAP-based interpretation.

Enterprise RAG / LLM product

Built a compliance-focused assistant designed for ~1,500 users with governance and traceability requirements.

Operational analytics products

Led Cancer Wait Times automation and a Streamlit MS prescribing analytics app using joins, fuzzy matching, and rule reconciliation.

LinkedIn

Professional profile, roles, and delivery context.

Google Scholar

Publications, citation trail, and research profile.

GitHub

Code repositories and implementation-oriented evidence.

Medium

Applied AI writing and knowledge-sharing outputs.

Substack

Writing profile and publication destination.

Stack Overflow

Public technical Q&A profile and community contributions.

Selected publications & talks

  • COLING 2022: Clinical NLP publication contribution (Paper page).
  • PhD Connect 2024: Speaker appearance connected to applied AI work in healthcare and data-driven decision support (Event page).

View the full speaking and publications profile →

Where I add value

  • Strategy to deployment: prioritise use cases, shape delivery plans, and turn technical work into deployed capability.
  • High-stakes AI delivery: build systems for regulated and operationally complex environments where trust matters.
  • Cross-functional leadership: align clinical/domain experts, product leads, data scientists, and engineering teams around outcomes.
  • Senior role fit: relevant for Applied AI Lead, Head of AI, AI Solutions Lead, Clinical AI Lead, Principal/Lead Data Scientist, and CTO-trajectory paths.