Lorena Espaillat Bencosme

Strategic Space Planner, Zaha Hadid Architects

Lorena Espaillat Bencosme

Lorena Espaillat Bencosme is a Strategic Space Planner at Zaha Hadid Architects, based in London, UK. She holds an MSc in Space Syntax (UCL Turner Prize for best dissertation), from the Bartlett School of Architecture, University College London.

Lorena’s research interest lies in understanding the impact of physical space and social phenomena on user behaviour in interior spaces, with particular focus on office spaces and the effect of micro level changes in spatial configuration on organisational culture.

Her current work involves workplace strategy and space planning with a distinctive approach based on in-house development and testing of analytic methods and tools to assess and predict workspace performance. Her work has been acknowledged by grants and awards from institutions such as the University College London, the Department of Global Affairs Canada, and the Pontificia Universidad Católica Madre y Maestra. She is an architectural practitioner with professional experience in the Dominican Republic and the United Kingdom.

Designing the workplace in the age of algorithms & machine learning

Designing the workplace in the age of algorithms & machine learning Successful companies have evolved into diverse, sustainable and resilient ecosystems, demanding architecture and design to be more flexible and responsive than ever. Architects have to be able to effectively accommodate highly complex communication and collaboration networks in space, not only for the present workforce but also for future cultural changes.

As place-makers, and specifically workplace environments, there is particular interest in understanding environmental impact and what are the social and spatial factors that drive performance. In this regard, increasing accessibility to computational power is allowing us to gather and process large datasets which later allow to identify the key parameters that constantly affect productivity and wellbeing. It is argued that algorithms and machine learning are creating unprecedented opportunities to design exemplary workplaces and ultimately to create workplaces that best suit overall and individual wellness needs and performance drivers.

This presentation will demonstrate current advanced computational analytics, the power of data and algorithmic design to drive better decision making for both designers and users, with examples and real-world applications. Furthermore, it will provide insights into ZHAs’ Analytics & Insights’ in-house tool development strategies which simultaneously simulate and visualise the unique preferences of thousands of individual users as well as instantaneously analyse user benefits of multiple design options.

The idea of responsiveness in workplace design is explored by looking into ‘self-learning environments’ which encompasses most of the research and practical work done in the last couple of years. Demonstrating our current workflow, a system integrating a multitude of parameters and influences, able to recursively react to new input.

Some of the key insights would be simulation techniques for environmental impact, like daylight influence and user specific properties like visibility, connectivity and views, as well as IoT sensing in three of ZHA AIs’ laboratories, sensing occupancy, movement density patterns, and environmental factors. Ultimately simulation and sensing is used to build up a data set of spatial information, which is then integrated, informing a predictive space planning tool.

Key Takeaways:

  • Examples and real-world applications of advanced computational analytics, the power of data and algorithmic design to drive better decision making for designers and users.
  • Insights into ZHAs’ Analytics & Insights’ in-house tool and development strategies
  • How to use simulation and sensing to build up a data set of spatial information to inform predictive space planning tools.

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