Paul Connolly

Developed statistical reports and interoperable health-data transformations using SNOMED CT, LOINC and HL7 FHIR to support clinical policy and national EHDS projects. Managed validation and synthesis of evidence for healthcare data guidance and stakeholder collaboration across national/EU bodies.

Led statistical programming for national health and social reports using SAS (Base, PROC SQL, MACRO), designing and automating data pipelines that delivered quality-controlled outputs for policy and public release. Drove SILC health indicator analyses and reproducible workflows.

Delivered validated SAS-based TLFs and submission-ready datasets for HIV clinical trials supporting WHO prequalification, acting as lead programmer across study phases and regulatory submissions.

Led statistical analysis for stability studies and GMP documentation, developed validated SAS macros for capability analysis and DOE, and supported HPRA and FDA audit processes to ensure GxP-compliant reporting.

Delivered statistical analyses on older-adult health outcomes for TILDA using R and SAS, designing and validating regression and predictive models and collaborating with the Dept. of Health, HSE, and HRB.

Conducted EPSRC-funded hydrodynamic modelling research in sports engineering, developing analytical tools and managing data in MATLAB, MySQL and SPSS, with industry collaboration (Speedo, British Swimming).

Activities and societies: EPSRC-funded research; collaboration with Speedo and British Swimming; research assistant duties involving MATLAB, MySQL, and SPSS.

Doctoral research in hydrodynamic modelling of swimmer wake asymmetry; thesis currently in resubmission phase for final award consideration.

Professional Diploma in Data Science coursework covering machine learning, statistical modelling, R programming, and data visualisation.

BSc (Hons) in Health and Performance Science with coursework and projects focused on health outcomes and performance metrics.