Biography
Dr Zeynep Duygu Tekler is a Senior Research Associate in the Department of Engineering Science, and Junior Research Fellow at Kellogg College, University of Oxford. Her research focuses on occupant-centric smart building technologies and machine learning approaches for high-performance buildings. Her postdoctoral work involves developing scalable building technologies and data-driven approaches for analysing building energy consumption and occupant behavior in the built environment.
Before joining Oxford, she held a Postdoctoral Research Fellow position in the Department of the Built Environment at the National University of Singapore. She earned her PhD in Engineering Product Development at the Singapore University of Technology and Design (SUTD) under the SUTD-MIT International Design Center in 2021. Prior to her PhD, she received both her MSc and BSc in Industrial Engineering from Isik University, where she received the honor of Valedictorian.
Dr Tekler’s research has been published in leading journals including Building and Environment, Energy and Buildings, and Applied Energy. She is a Fellow of the Higher Education Academy and recipient of the prestigious SINGA PhD scholarship. At Oxford, she was named first runner-up at the Oxford Energy Hackathon for Net Zero and awarded the Seal of Excellence for her Marie Curie Fellowship proposal. She received the SUTD Best Research Project Award for her patented work, Plug-Mate, on an innovative occupancy-driven energy management system. She was also the winner of the SUTD 3MT Competition and the Asia-Pacific finalist in 2020.
Research Interests
- Internet of Things
- Occupancy Detection
- Thermal Comfort
- Smart Energy Management Systems
- Machine Learning
Current Projects
- The EDOL is an £8.7 million UKRI-EPSRC funded program, dedicated to collecting detailed longitudinal data on household energy use and associated socio-technical variables within the UK. This five-year initiative is focused on establishing a national energy data platform, designed to facilitate the country's transition towards net-zero carbon emissions.
- The openBES is an open-source building energy simulation tool (openBES) to inform decision-making for cost-effective and low-carbon interventions. As part of Oxford’s EPSRC Impact Acceleration Account (IAA), it will expand its impact to educational, professional, and non-technical audiences.
Research Groups
Related Academics
Publications
Deep learning based predictive early warning system for heat and ventilation risks in naturally ventilated hospital wards
Tekler ZD, Roberts B, Low R & Cook M (2026), Indoor Air 2026 Proceedings
BibTeX
@inproceedings{deeplearningbas-2026/10,
title={Deep learning based predictive early warning system for heat and ventilation risks in naturally ventilated hospital wards},
author={Tekler ZD, Roberts B, Low R & Cook M},
booktitle={19th International Conference of the International Society of Indoor Air Quality and Climate (INDOOR AIR 2026)},
year = "2026"
}
Optimising Sensor Deployment in Open-Plan Offices Using Ordinary Kriging for Spatial Indoor Temperature Mapping
Suliman A, Tekler Z, Topouzi M, Lizana J & Low R (2026)
BibTeX
@inproceedings{optimisingsenso-2026/7,
title={Optimising Sensor Deployment in Open-Plan Offices Using Ordinary
Kriging for Spatial Indoor Temperature Mapping},
author={Suliman A, Tekler Z, Topouzi M, Lizana J & Low R},
booktitle={Indoor Air 2026},
year = "2026"
}
COAST: Congestion-Aware Start-Time Recommendations for Carbon-Aware HPC Jobs
Feng W, Dasgupta A, Tekler ZD, Ahuja S, Zheng J et al. (2026)
BibTeX
@misc{coastcongestion-2026/7,
title={COAST: Congestion-Aware Start-Time Recommendations for Carbon-Aware HPC Jobs},
author={Feng W, Dasgupta A, Tekler ZD, Ahuja S, Zheng J et al.},
year = "2026"
}
Poster Abstract: Can LLMs Replace In-domain Training Data? Cold-Start Thermal Comfort Prediction Across Unseen Building Contexts
Tekler ZD & Low R (2026), 573-574
BibTeX
@inproceedings{posterabstractc-2026/6,
title={Poster Abstract: Can LLMs Replace In-domain Training Data? Cold-Start Thermal Comfort Prediction Across Unseen Building Contexts},
author={Tekler ZD & Low R},
booktitle={Proceedings of the 2026 ACM Sustainability Week},
pages={573-574},
year = "2026"
}
COAST: Congestion-Aware Start-Time Recommendations for Carbon-Aware HPC Jobs
Feng W, Dasgupta A, Tekler ZD, Ahuja S, Zheng J et al. (2026)
BibTeX
@misc{coastcongestion-2026/,
title={COAST: Congestion-Aware Start-Time Recommendations for Carbon-Aware HPC Jobs},
author={Feng W, Dasgupta A, Tekler ZD, Ahuja S, Zheng J et al.},
year = "2026"
}
Occupancy monitoring approaches: learnings from literature and current research
Topouzi M, Suliman A & Tekler Z (2025)
BibTeX
@inproceedings{occupancymonito-2025/4,
title={Occupancy monitoring approaches: learnings from literature and current research},
author={Topouzi M, Suliman A & Tekler Z},
booktitle={CIBSE IBPSA-England Technical Symposium 2025},
year = "2025"
}
Clustering household electricity and gas load profiles in the UK using smart meter and survey data
Tekler ZD (2024), Proceedings of uSim Conference 2024: 4th uSim Conference of IBPSA-Scotland
BibTeX
@inproceedings{clusteringhouse-2024/11,
title={Clustering household electricity and gas load profiles in the UK using smart meter and survey data},
author={Tekler ZD},
booktitle={ uSIM2024 – Shaping Net Zero Policies with Building Simulation},
year = "2024"
}
Data-efficient Comfort Modeling: Active Transfer Learning for Predicting Personal Thermal Comfort using Limited Data
Tekler ZD, Lei Y & Chong A (2024), Energy and Buildings, 114507-114507
BibTeX
@article{dataefficientco-2024/7,
title={Data-efficient Comfort Modeling: Active Transfer Learning for Predicting Personal Thermal Comfort using Limited Data},
author={Tekler ZD, Lei Y & Chong A},
journal={Energy and Buildings},
pages={114507-114507},
publisher={Elsevier},
year = "2024"
}
Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design
Ono E, Tekler ZD, Lam KP, Jin Y, Yan D et al. (2024), Building and Environment, 111739-111739
BibTeX
@article{evaluatingthese-2024/6,
title={Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design},
author={Ono E, Tekler ZD, Lam KP, Jin Y, Yan D et al.},
journal={Building and Environment},
number={111739},
pages={111739-111739},
publisher={Elsevier BV},
year = "2024"
}
Evaluating different levels of information on the calibration of building energy simulation models
Cheng S, Tekler ZD, Jia H, Li W & Chong A (2024), Building Simulation, 17(4), 657-676
BibTeX
@article{evaluatingdiffe-2024/4,
title={Evaluating different levels of information on the calibration of building energy simulation models},
author={Cheng S, Tekler ZD, Jia H, Li W & Chong A},
journal={Building Simulation},
volume={17},
pages={657-676},
publisher={Springer Nature},
year = "2024"
}
