.jpeg)
Orcun Koral Iseri, PhD
Postdoctoral Researcher | Building Physics & AI/ML for BEM/UBEM
Concordia University, Montreal
Research Focus
- Urban Building Energy Modeling (UBEM)
- AI/ML for Sustainability
- Data-Driven Occupancy Modeling
- Building Information Modeling (BIM)
- Climate-Resilient Built Environments
- Deep Learning (CVAEs, Transformers, GNNs)
- Stochastic Occupancy Modeling
- Building Stock Turnover Modeling
- K-Means Archetype Characterization
- Multi-objective Optimization (NSGA-II)
- Computer Vision for Occupancy Classification
- Building Stock Simulation & HPC Workflows
Background
I graduated from Yasar University, Architecture Department in 2016, and completed my MSc at the same university in 2018. I then went on to complete my PhD at Middle East Technical University in September 2024. I am currently working as a Postdoctoral Fellow at Concordia University in Montreal.
I specialize in Urban Building Energy Modeling (UBEM), AI/ML for sustainability, and data-driven occupancy modeling of climate-resilient built environments. I am currently developing a modular Neighborhood Unit (NU) framework for Canadian urban energy planning aligned with NECB / NBC standards and net-zero community design in cold climates.
My methodological contributions span stochastic occupancy modeling, building stock turnover modeling, Sobol sensitivity analysis, k-means archetype characterization, and deep learning. I am fluent in Python (TensorFlow, PyTorch, Scikit-learn, SciPy, SALib) and R, with HPC workflows for large-scale building stock simulation.
I have published in Q1 journals (Energy and Buildings, Automation in Construction, Advanced Engineering Informatics) and serve as a peer reviewer for Scientific Reports. I led a TUBITAK 1001-funded research project (2020–2023) and co-mentored Master's-level students in UBEM applications at METU.