Data Science and Machine Learning are research themes that are rapidly gaining attention in the fields of architecture, urbanism, and design. The increasing availability of data and advances in computational methods have made it possible to apply data science and machine learning techniques to a wide range of problems in the built environment. Data science and machine learning can be used to analyze large and complex datasets, such as building performance data, urban sensor data, and social media data. This can help architects, urban planners, and designers to make more informed decisions, optimize designs, and improve the performance of buildings and cities.

One of the key applications of data science and machine learning in architecture, urbanism, and design is in the area of building performance simulation and analysis. Machine learning algorithms can be used to analyze large amounts of building performance data, such as energy consumption data, and identify patterns and correlations. This can help architects and engineers to optimize the design of buildings for energy efficiency, comfort, and indoor air quality. Additionally, data science can also be used to simulate the behavior of a building or a city under different environmental conditions, such as weather and climate change. This can help architects, urban planners, and designers to design more resilient and sustainable buildings and cities. In the field of urbanism, data science and machine learning can be used to analyze large amounts of data on transportation, demographics, and land use, to identify patterns and correlations that can be used to inform city planning and design.

Overall, data science and machine learning are research themes that have the potential to transform the way we approach the design and management of the built environment. These technologies can help architects, urban planners, and designers to make more informed decisions, optimize designs, and improve the performance of buildings and cities.