Usman Ali —
AI & data science for Ireland's energy transition.
Computer & data scientist with nine+ years applying AI and machine learning to national-scale sustainability and energy systems. PhD from University College Dublin. Currently leading AI and data analytics for Ireland's National Building Renovation Plan at SEAI, while supervising research as an Adjunct Assistant Professor at UCD — bridging policy-grade industry delivery with academic research.
Turning national-scale data into evidence for Ireland's energy transition — bridging machine learning research and real-world policy delivery.
I'm a computer and data scientist with a BS and MS in Computer Science and over nine years of experience applying AI, machine learning and large-scale national data analytics to sustainability and energy systems. My PhD from University College Dublin centred on AI and data-driven methods applied to national datasets covering over three million building records — work that earned the Best Research Paper of the Year in both 2023 and 2024 from Energy and Buildings (Elsevier).
I currently lead AI and data analytics for Ireland's National Building Renovation Plan at the Sustainable Energy Authority of Ireland (SEAI), building ML pipelines and decision-support frameworks at national scale on Azure. Alongside this, I hold an Adjunct Assistant Professor position at UCD, supervising PhD and MSc students and teaching AI for energy systems. Across seven years of university teaching I've held full module responsibility for AI, software engineering and database systems — and I remain committed to open, reproducible, FAIR research that connects students with real national datasets.
AI systems built for national-scale impact.
From LLM-powered pipelines to graph AI on national datasets — the applied AI skillset behind Ireland's energy-policy analytics.
Where AI meets national energy policy.
Four connected threads — from award-winning building-energy modelling to graph AI and international collaboration — all aimed at evidence-based decarbonisation.
National-Scale AI for Energy Policy
Leading ML pipelines across 1M+ property records for Ireland's National Building Renovation Plan at SEAI — XGBoost, random forest and ensemble models on Azure, informing decarbonisation policy directly.
Urban Building Energy Modelling & GIS
Award-winning, most-cited data-driven methods for national building-stock energy prediction and retrofit decision-support — validated against physics-based simulation.
Knowledge Graphs & Graph-Based AI
A Neo4j knowledge graph integrating 8M+ data points across building information, energy simulation and national datasets — merging graph AI with physics-based models.
International AI Collaborations
Joint research with the University of Pennsylvania and Ulster University on AI-driven models for residential energy efficiency, funded by SEAI, SFI and the US NSF.
Nine years across industry & academia.
Currently splitting time between national energy policy delivery at SEAI and academic supervision at UCD — with a research and teaching career spanning three countries.
Senior Data Analyst — National Energy
Industry Jul 2024 – Present- Lead AI and data analytics for Ireland's National Building Renovation Plan — ML pipelines across 1M+ records on Azure
- XGBoost, random forest and ensemble models for national retrofit prioritisation and decarbonisation impact assessment
- Develop AI decision-support frameworks integrating national building datasets, BER certificates and energy consumption records
- Build open, reproducible analytics infrastructure — code on GitHub, data registered with persistent identifiers
- Collaborate with government departments, local authorities and international researchers on evidence-based energy policy
Adjunct Assistant Professor
Academia Jul 2024 – Present- Supervise PhD and MSc students in machine learning, AI for energy systems and sustainability data science
- Contribute to postgraduate curriculum development, integrating research-led data analytics and ML tools
- Deliver guest seminars on AI methods, national-scale data systems and open science practice
Senior Energy Researcher & Data Scientist
Academia 2022 – Present- Developed ML frameworks processing 2M+ building data points for national-scale sustainability analysis
- Led international AI collaboration with University of Pennsylvania and Ulster University (NSF-funded)
- Designed the Neo4j knowledge graph integrating 8M+ data points for national-scale AI analytics
Consultant Data Scientist
Industry 2022 – 2024- Developed a multi-scale ML platform for decarbonisation analysis across Northern Ireland, informing regional energy policy
- Engineered ML forecasting algorithms integrating building-level and national energy consumption data
- Designed a Python-based AI decision-support dashboard deployed to regional government stakeholders
Senior Energy Policy Researcher
Industry 2021 – 2022- Led review and enhancement of Ireland's national dwelling energy assessment methodology (DEAP)
- Developed and validated a PV integration methodology adopted into national building energy certification standards
PhD Researcher — Data-Driven Building Energy Systems
Academia 2017 – 2020- Developed AI/ML frameworks for Ireland's national residential building stock — 3M data points across geometry, census, BER and climate data
- Built an open-source Python surrogate ML platform validated against EnergyPlus physics-based simulation
- Applied clustering (k-means, hierarchical) for national building-archetype identification
Teaching Assistant & Demonstrator
Academia 2017 – 2020- Delivered labs and tutorials for Introduction to Engineering Computing, Advanced Data Structures and Data Analysis
Data Analyst
Industry Jan 2019 – Dec 2019- Developed ML predictive models for Ireland's national Building Energy Rating (BER) system
Lecturer
Academia 2013 – 2015- Full module responsibility for 8 undergraduate modules — AI, Databases, Software Engineering, Data Structures, OOP
- Taught cohorts of 40–80 students; introduced Python, MATLAB and SQL as practical components
Senior Research Associate
Academia 2008 – 2016- Smart Grid energy management, energy data analytics, and AI for energy-demand forecasting
PhD — Machine Learning & Data Science
University College Dublin 2017 – 2020- Thesis: "A Data-Driven Approach for Multi-Scale Building Energy Modeling for Analysis, Planning and Support Decision Making"
- Supervisors: Prof. James O'Donnell and Prof. Eleni Mangina
MS — Computer Science
Lahore University of Management Sciences (LUMS), Pakistan 2011 – 2013- AI, machine learning, data mining, software engineering, algorithms
BS — Computer Science
International Islamic University, Islamabad, Pakistan 2004 – 2008- Algorithms, data structures, AI, databases, software engineering, OOP
Peer-reviewed & conference research.
1,727+ citations, h-index 15, across 30+ publications in Applied Energy, Energy and Buildings and leading building-simulation venues. Full record on Google Scholar.
Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis
Energy & Buildings, Vol. 246
A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making
Applied Energy, Vol. 279
Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach
Energy and Buildings, Vol. 303
A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings
Applied Energy, Vol. 267
A data-driven approach for multi-scale building archetypes development
Energy and Buildings, Vol. 202
Urban residential building stock synthetic datasets for building energy performance analysis
Data in Brief, Vol. 53
Research that shapes national policy.
From live government programmes to internationally-funded research collaborations.
National Building Renovation Plan — AI Pipelines
ML pipelines across 1M+ property records on Azure — XGBoost, random forest and ensemble models prioritising retrofit investment and decarbonisation impact at national scale.
National Building Stock Knowledge Graph
A graph database integrating 8M+ data points across building information, energy simulation and national datasets — merging graph-based AI with physics-based energy models.
International AI & Sustainability Collaboration
Joint research with the University of Pennsylvania and Ulster University on AI-driven models for residential energy efficiency and greenhouse gas reduction.
National DEAP Methodology Review
Led review and enhancement of Ireland's national dwelling energy assessment methodology; developed a PV integration methodology now adopted into national BER standards.
The stack behind the research.
Languages & Programming
AI / ML Stack
Cloud & Data Engineering
Geo & Domain Tools
Databases & Graph AI
Awards, funding & certifications.
Best Research Paper of the Year 2023 & 2024
Energy and Buildings (Elsevier) — Publisher & Editor-in-Chief Selection
Top Cited Paper Award (2020)
UCD School of Engineering
2nd Prize — UCD Research Communicating Impact
Data Visualisation category
2nd Prize (USD 500) — SOFTEC
13th National & 4th International Software Exhibition
Research Funding
SEAI, Science Foundation Ireland (SFI), US NSF, Dept. for the Economy (NI)
Google Project Management Specialization
Google Certification, 2023
Open to senior AI/data science & academic collaborations.
Whether it's national-scale energy analytics, an academic partnership, or an industry AI role applying machine learning to sustainability — I'd like to hear from you.