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Personal profile


Leila Darvishvand is a dedicated Ph.D. researcher in the School of Architecture and the Built Environment, specializing in the application of machine learning methods to advance building energy modeling for characterizing building stock. Over the past few years, she has served as a research assistant, focusing on the cutting-edge field of thermal storage technologies. Leila's work revolves around harnessing the power of Artificial Neural Networks and Genetic Algorithms to optimize thermal storage heat exchangers under diverse conditions. Her research places a strong emphasis on pioneering design concepts and conducting rigorous experimental investigations.


In addition to her contributions in academia, Leila has made significant strides in the industry, excelling as a Mechanical Engineer within the construction sector. Her academic journey boasts impressive achievements, including the successful completion of M.Sc. and B.Sc. degrees in Mechanical Engineering, accompanied by outstanding theses. Notably, she earned recognition as one of the "Excellent Students" at the esteemed University of Tehran, solidifying her status as a high-achieving scholar and researcher in the field of building energy modeling and thermal storage technologies. Leila has also made her mark with publications in high-prestigious journals, further establishing her as a prominent figure in the academic and research community.

Research Interests

Building energy modelling

Building Stock energy modelling

Phase change materials

Artificial neural network


Data Analysis

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 11 - Sustainable Cities and Communities
  • SDG 13 - Climate Action


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Collaborations and top research areas from the last five years

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