This image shows Diellza Elshani

Diellza Elshani

M. Sc., B. Arch.

Research Associate
2021 - Current


Keplerstraße 11
70174 Stuttgart
Room: 10.32

Elshani, Diellza; Reinhard Koenig; Duering, Serjoscha; Schneider, Sven; Chronis, Angelos (2021): Measuring Sustainability and Urban Data Operationalization. An integrated computational framework to evaluate and interpret the performance of the urban form. In Caadria. Hong Kong.

Elshani, D., Vititneva, E., Gilmanov, A., Koenig, R., Schneider, S., et al. 2019. Rural Urban Transformation: Parametric Approach on Metabolims-Based Planning Strategies in Ethiopia. SimAUD: Symposium on Simulation for Architecture & Urban Design.Vienna. |

Blasius,B., Beenen, P., Brandt, M., Elshani, D., et al. 2019. Tirana. Architecture as a political actor. Bauhaus University Weimar. |

Laboratory Course Artificial Intelligence: Semantic Digital Twins  - WS 2023/2024
BIM Seminar - SS 2023 & WS 2023/2024

Diellza Elshani is a research associate and doctoral candidate at the Institute for Computational Design and Construction at the University of Stuttgart. She holds a Master of Science in Integrated Urban Development & Design from the Bauhaus University of Weimar and a Bachelor of Architecture from Mimar Sinan Fine Arts University in Istanbul.

Before coming to ICD, Diellza was a researcher at the City Intelligence Lab (CIL) of the Austrian Institute of Technology (AIT), focusing on cognitive urban design computing. Her Master Thesis is written at the Chair of Computational Architecture at the Bauhaus University Weimar, collaborating with the AIT. It presents a computational framework to measure, operationalize and interpret the performance of the urban form using generative design methods.

She gained architectural experience in Germany, Austria, Turkey, and Kosovo; she has mentored workshops and hackathons; took part in summer schools, conferences, film festivals, and many extracurricular activities which sculpted her as a human, designer, and scientist.

Her research interest lies in using computational methods in co-design and developing ontologies that improve the interoperability in multidisciplinary architectural design.

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