AI-Integrated Research and Design

Agent-based and agentic workflows using APIs to automate or coordinate research tasks, data processing, coding, literature handling, scenario generation and iterative analysis.

Topic modelling, semantic clustering, sentiment and stance analysis, entity extraction, text classification and coding assistance for interview, survey and policy datasets.

Supervised and unsupervised learning, clustering, classification, regression, feature importance, anomaly detection and predictive modelling.

Text-to-3D, image-to-3D, parametric concept generation and spatial iteration for early-stage architectural design.

Analysing streetscapes, facades, materials, land use, public space, thermal images, construction progress and post-occupancy conditions.

Combining GIS, remote sensing and ML for urban analysis, climate risk, accessibility, morphology, land-use patterns, green infrastructure and environmental exposure.

A particularly distinctive area, bringing AI methods directly into Life Cycle (Sustainability) Assessment practice.

Linking publications, standards, material databases, policy documents, stakeholder evidence and project data into structured research systems.

Combining text, images, drawings, diagrams, GIS, sensor data and numerical datasets.

Analysis of workshop outputs, citizen science data, co-design material, maps, drawings, photographs and open-text responses.

For buildings, urban systems, energy use, retrofit scenarios and behavioural modelling.

Multi-objective optimisation across carbon, cost, energy, health, wellbeing, daylight, spatial quality and social criteria.

Studying how researchers, designers, students and communities work with AI, where judgement remains human, and how trust, interpretation and representational bias are affected.