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.

Building energy modelling

Building Stock energy modelling

Phase change materials

Artificial neural network

Optimization

Data Analysis