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Smashing Boundaries – How Sara & David Are Rethinking Energy and Materials
This week, we sat down with Sara Abouebeid and David Sindelar, two of our project assistants in the Sustainable Built Environments research group, to learn more about their work, research experiences, and perspectives on sustainability. From energy resilience to material stock mapping, Sara and David share their insights on the projects they are contributing to and what excites
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Interview with Shuang after her Licentiate
Congratulations to Shuang Wang on successfully defending her Licentiate thesis titled: “Characterizing the construction sector’s potential as secondary plastic provider.” Her research focuses on the circularity of plastics in the construction sector, a critical area for sustainability in the built environment. We sat down with Shuang to ask some questions after the licentiate defence. In this interview,
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Interview with Xinyue on her research visit at the University of Washington
We recently caught up with Xinyue about her research visit at the University of Washington. During her stay, she explored innovative machine learning methods to enhance building sustainability—not only in terms of energy efficiency but also in indoor air quality and urban energy dynamics. In this interview, Xinyue discusses her motivation for the visit, insights
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Interview with Toivo on his research visit at SEED, KTH
We sat down with Toivo to speak about his recent research visit at SEED, KTH. His work focuses on bridging the gap between building-level LCA approaches and the strategic environmental work needed at the city and regional levels. In this interview, Toivo shares what inspired his visit, key insights from collaborating with experts in environmental
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Xinyue has defended her licentiate thesis
On June 10th, Xinyue Wang presented at her Licentiate seminar defending her thesis entitled ‘Towards Machine Learning Application in Early-Stage Building Energy Optimization’. Her thesis discusses how to develop a machine learning building energy prediction model to substitute the traditional simulation engines in early stage optimization workflow for higher efficiency. Professor Paul Sheperd, from the University


