Professor Lina Stankovic
Electronic and Electrical Engineering
Personal statement
Personal statement
My research expertise is in Signal and Information Processing (inc. AI technologies and machine learning), with focus on representation, processing, temporal and spatial information mining, and data management for a range of signal types acquired from sensors. My key strength is in extracting meaningful information from real data, including electrical, seismic, health/biological and other environmental sensor data, developing algorithms to handle dynamic and complex data that is often sparse, noisy, heterogeneous and multimodal, gathered from distributed sources. My research is multi-disciplinary by nature and has a strong focus on tackling emerging challenges pertaining to climate change and United Nations Sustainable Development Goals, working closely with international collaborators and non-academic stakeholders to lead innovation in the field.
My work is particularly aligned with the following university strategic research themes (Measurement Science & Enabling Technologies, Energy, Health & Well-Being)
I am currently Deputy Head of Department Learning & Teaching.
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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
- Quantifying opportunities of reducing 𝐶𝑂2 footprint of dairy farms with different on-site renewable generation and regional 𝐶𝑂2 grid mix
- MacKenzie Jack, Callaghan John, Stankovic Vladimir, Stankovic Lina
- UK AI Research Symposium, pp. 1-2 (2026)
- Residential load archetype extraction under low-carbon technology adoption using two-stage DTW meta-clustering
- Vavouris Apostolos, Stankovic Lina, Stankovic Vladimir
- Energy and Buildings Vol 373 (2026)
- https://doi.org/10.1016/j.enbuild.2026.118366
- 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
- A mixed methods load segmentation approach for informing flexibility services in energy systems
- Stankovic Lina, Gibson Samuel, Stankovic Vladimir
- 2026 9th International Conference on Green Energy and Environment Engineering (CGEEE 2026) 9th International Conference on Green Energy and Environment Engineering E3S Web of Conferences Vol 742 (2026)
- https://doi.org/10.1051/e3sconf/202674202001
- Physics-guided GRU model for stable multi-horizon displacement forecasting
- Parasyris Apostolos, Stankovic Lina, Stankovic Vladimir
- EEITE 2026 - 7th International Conference in Electronic Engineering and Information Technology IEEE 7th International Conference in Electronic Engineering & Information Technology 2026 (2026)
- https://doi.org/10.1109/EEITE69609.2026.11679818
- 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
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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
- Quantifying temporal and spatial causalities between climate change and slope failures through the use of Advanced AI
- Speaker
- 11/9/2025
- Automated Exploration and Analysis of Seismological Data Streams with Advanced AI-based technologies
- Examiner
- 11/9/2025
Projects
- A graph-based data-driven framework for equitable electric vehicle placement (SENSE Research Grant)
- Stankovic, Lina (Principal Investigator) Stankovic, Vladimir (Co-investigator)
- 01-Jan-2026 - 30-Jan-2027
- 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
- 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
- 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
- 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
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Contact
Professor
Lina
Stankovic
Electronic and Electrical Engineering
Email: lina.stankovic@strath.ac.uk
Tel: 548 2704