Professor Lina Stankovic
Electronic and Electrical Engineering
Personal statement
Personal statement
My research focuses on signal and information processing, artificial intelligence, and machine learning, with a particular interest in extracting meaningful information from complex, real-world sensor data. I work across disciplines to develop data-driven solutions for challenges in energy, geoscience, and environmental monitoring, with a strong emphasis on climate action and sustainability. My work combines methodological innovation with close engagement with industry, government, and international partners, translating advanced analytics into practical tools and evidence that support decarbonisation, resilient infrastructure, and societal wellbeing. As Deputy Head of Department for Learning and Teaching, I am also committed to enhancing engineering education and supporting student success across our diverse learning community.
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Prize And Awards
- Strategic Themes Research and Knowledge Exchange Impact Prize: Centre for Intelligent Dynamic Communications (CIDCOM) Team
- Recipient
- 25/8/2025
- Dumfries and Galloway Chamber of Commerce Net Zero Award
- Recipient
- 6/6/2025
- Finalist of the World first 10K Best paper Award (Top 3%)
- Recipient
- 12/7/2017
- Paper awarded top 5% by reviewers in Eusipco-2010 conference and invited for journal publication.
- Recipient
- 8/2010
Qualifications
PhD and BEng (Hons) in Electronic Communication Systems from Lancaster University
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Publications
- Enhancement of hydrological time series prediction with Real-World Time Series Generative Adversarial Network-based synthetic data and deep learning models
- Dodig Ana, Stankovic Vladimir, Stankovic Lina, Stojkovic Milan
- Environmental Modelling and Software Vol 204 (2026)
- https://doi.org/10.1016/j.envsoft.2026.107037
- The more the merrier : the effects of data volume and transfer learning on creating generalisable deep learning PAM detectors
- Fonollosa Laia Garrobe, Gillespie Douglas, Stankovic Lina, Stankovic Vladimir, Rendell Luke
- Marine Mammal Science Vol 42 (2026)
- https://doi.org/10.1111/mms.70265
- Classifying bioacoustic data without individual call annotations using temporal convolutional networks and feature extractors
- Fonollosa Laia Garrobe, Gillespie Douglas, Stankovic Lina, Stankovic Vladimir, Rendell Luke
- Journal of the Acoustical Society of America Vol 160, pp. 1159-1171 (2026)
- https://doi.org/10.1121/10.0044583
- Motivating the need for automated load scheduling for dairy farms with on site renewable generation
- Callaghan John, Stankovic Lina, Stankovic Vladimir
- AIBIO-UK 3rd Annual Conference 2026, pp. 9 (2026)
- Uncovering hidden demand flexibility using non-intrusive load monitoring (NILM) : a case for Southern Africa – Namibia
- Mollel Rachel Stephen, Elombo Andreas, Stankovic Lina, Stankovic Vladimir, Hambata Jona, Thiel Gunther
- Proceedings of the 12th International Conference on Energy Efficiency in Domestic Appliances and Lighting 12th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL’24) Springer Proceedings in Energy, pp. 427–447 (2026)
- https://doi.org/10.1007/978-3-032-10974-3_27
- ChargeDEM : geodemographic aware EV charging infrastructure placement for enhanced site selection using graph neural networks
- Batic Djordje, Stankovic Vladimir, Stankovic Lina
- Proceedings of the 12th International Conference on Energy Efficiency in Domestic Appliances and Lighting 12th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL’24) Springer Proceedings in Energy, pp. 571–586 (2026)
- https://doi.org/10.1007/978-3-032-10974-3_37
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Teaching
Current classes:
1st year Engineering Design for Software Development inc. Python programming
4th/5th year Information Transmission and Security, inc. Digital communications principles, channel coding
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Research Interests
- Smart meter energy analytics, including load disaggregation, prediction and profiling
- AI for the Decarbonisation of the Built Environment, Agriculture and Transport
- Environmental impact assessment of consumption cycle of Food Systems and Appliances inc. Electric Vehicles
- Detecting, classifying, understanding and characterising subsurface processes arising from natural slopes for landslide mitigation and human activities, such as geothermal extraction
- Model-fitting/system characterisation
- Sensor deployment and data acquisition
Professional Activities
- Computational Intelligence Techniques for Observable Smart Grid and Sustainable Energy Systems, part of the IEEE World Congress on Computational Intelligence (WCCI-2026)
- Participant
- 21/6/2026
- UKESF Girls into Electronics 2026
- Speaker
- 11/6/2026
- Learning from Multiple Data Modalities: Novel Model Architectures, Algorithms, and Applications to Healthcare Challenges
- Examiner
- 10/12/2025
- Towards Real-World NILM: Analytical and Empirical Procedures for Assessing Comparability and Reproducibility
- Examiner
- 26/11/2025
- Automated Exploration and Analysis of Seismological Data Streams with Advanced AI-based technologies
- Examiner
- 11/9/2025
- Quantifying temporal and spatial causalities between climate change and slope failures through the use of Advanced AI
- Speaker
- 11/9/2025
Projects
- CLEAN DAIRY - AI-Driven Low-Carbon Technologies for Net-Zero Dairy: Enhancing Productivity and Energy Security
- Stankovic, Lina (Principal Investigator) Stankovic, Vladimir (Co-investigator)
- 01-Jan-2026 - 31-Jan-2027
- Digital Dairy Chain Innovation Voucher: An AI data-driven reporting dashboard for the dairy sector
- Stankovic, Lina (Principal Investigator) Stankovic, Vladimir (Co-investigator) Vavouris, Apostolos (Researcher)
- This project will develop a cost-effective (by leveraging on bespoke open-source agri software) AI-powered energy analytics dashboard designed for dairy farms, integrating real-time and historical energy data to provide automated insights into consumption, renewable generation, and cost/carbon-savings opportunities. By leveraging machine learning, the system will identify inefficiencies, predict energy demand, and recommend optimisation strategies to reduce costs and carbon footprint. The dashboard will process data from smart meters, IoT devices, and renewables, offering actionable insights through a user-friendly interface. Farm owners will receive data-driven recommendations to maximise renewable energy use, optimise time of use, and enhance overall efficiency. Further to that, the dashboard will support recommendations for installing energy saving technologies, altering processes or equipment to save energy and reduce their carbon impact. This innovative solution aims to streamline energy management in dairy farming, supporting sustainability and financial savings through AI-driven decision-making.
- 01-Jan-2025 - 30-Jan-2025
- JED-AIs: Justice, Energy, Demand flexibility and AI for Sustainability (UKRI Cross Research Council Resp Mode)
- Stankovic, Vladimir (Principal Investigator) Stankovic, Lina (Co-investigator)
- 01-Jan-2025 - 31-Jan-2026
- Graph Embedding-Based Adaptive Early-Warning Model for Microseismic Monitoring of Open-Pit Slopes in Heterogeneous Geological Settings
- Stankovic, Lina (Principal Investigator) Stankovic, Vladimir (Principal Investigator)
- 01-Jan-2025 - 31-Jan-2026
- Picture-IT Create: Generative AI to support Diverse Learning Environments
- Stankovic, Lina (Principal Investigator) Stankovic, Vladimir (Co-investigator) Batic, Dorde (Researcher)
- 01-Jan-2024 - 31-Jan-2025
- REMINDA: Revolutionising Energy Monitoring in Non-Domestic Buildings with A.I
- Stankovic, Vladimir (Principal Investigator) Stankovic, Lina (Co-investigator)
- 01-Jan-2024 - 31-Jan-2025
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Contact
Professor
Lina
Stankovic
Electronic and Electrical Engineering
Email: lina.stankovic@strath.ac.uk
Tel: 548 2704