Data, models and systems used together for clear, reliable decisions.
Open to full-time roles, joint research, teaching and selected consulting work.
Focus on making complex data practical. This includes project design, modelling, data pipelines, and clear results for both technical and non-technical teams.
Work across Ireland, Italy and Germany has centred on large-scale data mining, modelling and system-aware analysis. Projects include agriculture, online content and intelligent systems, alongside teaching in third-level institutes.
Research in large-scale data mining produced a linked family of methods for structuring pre-processing, temporal alignment and improvement cycles. Three innovation licences cover these methods and show that the approach is original and adaptable to domains such as agriculture, security analytics, business data and education.
Interest in domain-independent analytical frameworks that support large datasets, streaming text, environmental signals, behavioural systems and satellite-derived sources. The aim is to design models that scale, remain interpretable and work under real conditions. This includes digital-twin style thinking, deployment-ready modelling pipelines and document intelligence.
Five years on the UCD–Origin Enterprises programme shaped an industry-facing view of data mining. Work involved fertiliser support, soil–crop–weather interactions and multi-season agricultural datasets.
Work at ADAPT and in the EU TRIVALENT project covered multilingual text, misinformation patterns and early risk signals.
Earlier projects included tumour growth models using cellular automata and work with neuro-computational signal recordings.
Teaching in data analysis, programming, databases and business intelligence builds skills in explanation and reproducible work. Earlier IT experience in servers, networks, virtualisation and testing supports practical deployment.
Research direction centres on methods that generalise across agriculture, environmental modelling, document analytics, behavioural data, security analytics, satellite information, health signals and DNA/genomics. The shared aim is to produce analytical tools that are scalable, interpretable and ready for use.
A mix of university teaching, applied research and earlier systems roles. Full details and dates appear in the CV.
Teaching data analysis, databases, programming and business intelligence across multiple campuses. Work includes lectures, labs, assessments and links to real datasets.
Large-scale agricultural data mining, fertiliser support models and multi-season datasets with soil, crop and weather data. Close links with agronomy and commercial teams.
Pipelines for multilingual text, misinformation and risk signals. Focus on data structure, traceability and consistent model behaviour.
Analysis of online radicalisation indicators, sentiment patterns and behavioural features in EU project settings.
System administration, network configuration, virtualisation, VOIP systems, software testing and hardware diagnostics. These roles built a strong base in operational environments.
Common ways to structure work in roles, collaborations or consulting, from analysis and dashboards to systems reviews and research support.
Used when datasets already exist and structured answers are needed.
Helpful for teams needing reliable, up-to-date views.
Suitable for logs, events and sensor streams that need structure.
For teams that need stable and predictable systems.
Used in academic and technical settings.
Helps when repeated work and checks take too much manual time.
Skills grouped by how they are used in projects, teaching and systems work.
Examples here are simplified for a public view. Full technical notes and reports are available on request.
Field and soil data from farms used to train models that support fertiliser timing and rate decisions. This reduced trial-and-error in the field and improved planning for agronomy teams.
Work on EU-funded projects that studied online content, focusing on hostility, disinformation and early signs of harmful group activity.
Support for modules in databases, programming, business intelligence and data analysis at third-level. Assessment design aligned with realistic data tasks.
Research in data mining produced linked frameworks such as SOC, SSL, LLS, ASDM, EDSE, TPM and PME. Three innovation licences cover large-scale pre-processing, advanced mining and the combined framework group.
These licences sit over a family of methods that can be adapted for sectors needing repeatable data work, from agriculture and retail to education and online content.
Open to collaboration with universities, research groups and companies that need structured data support, guest teaching or project supervision.
Interest in analytical frameworks that apply across agriculture, environmental systems, security analytics, behavioural data, satellite information, health analytics, DNA/genomics and document processing. Broader curiosity in AI, neuroscience, cognition, quantum ideas, satellite systems and human behaviour.
For full-time roles, joint research, teaching or selected consulting work, a short message with context is enough to begin. Helpful details include the sector, type of data and the main problem to improve.