Iraklis A. Klampanos

Senior Lecturer (Accociate Professor) in Data Engineering & Data Systems
Director of MSc in Data Science
School of Computing Science
University of Glasgow

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About Me 👾

I am a Senior Lecturer (Associate Professor) in Data Systems and Data Engineering at the University of Glasgow, where I direct the MSc Data Science programme. My research focuses on advancing AI-driven data engineering and intelligent systems to support scientific and environmental research, and complex workflows. With a background in multimodal AI and representation learning as a basis for next-generation foundation models, I focus on bridging the gap between data science and domain-specific challenges especially in the natural and computational sciences.

I have extensive experience in the preparation and implementation of large-scale, multi-party Research and Innovation projects, including Horizon 2020 and Horizon Europe initiatives, where I have served in various leadership and technical roles.

Before joining Glasgow (January 2025), I was a Researcher at NCSR “Demokritos” in Athens (2015-2024), heading the Intelligent Data-Intensive Systems group. There, I led research on AI methods for data engineering and held leadership roles in major European projects such as DARE, AI4Copernicus, Fair4Fusion, AI4Europe and DeployAI, contributing to the development of AI-ready platforms and tools for scientific and industrial applications.

Earlier, I worked as a Postdoctoral Research Associate at the University of Edinburgh's School of Informatics (2012-2015), where I contributed to architecture efforts in the FP7 VERCE project and to the design of dispel4py workflow library for data-intensive scientific computing. I also held research roles at the University of Glasgow (2008-2011) and earned my PhD in Computing Science here in 2007.

Research interests:

• AI for Data Engineering and Intelligence-Ready Platforms: Creating adaptive, AI-driven methods for managing and orchestrating large-scale, heterogeneous data and systems.
• Representation Learning and Multimodal AI for Scientific Data: Developing dependable AI methods and foundation models that learn reusable and extensible representations from complex scientific datasets and modalities.
• Trustworthiness and FAIRness for AI Methods and Data: Ensuring data remains Findable, Accessible, Interoperable, and Reusable throughout its lifecycle, particularly in AI-driven and data-intensive computing contexts.
• AI-Driven Earth Observation and Environmental Applications: Applying AI to satellite and sensor data for monitoring, modelling, and predicting environmental and climate phenomena.

Looking for PhD Students 🚀

I am actively seeking motivated and talented PhD candidates to join me at the University of Glasgow. If you are passionate about advancing AI-driven data engineering, representation learning, and their applications in scientific research, please reach out to discuss potential research topics and opportunities.

For scholarship opportunities and financial support, visit the following resources:

• UofG Scholarships & Funding Portal
• School of Computing Science Studentship information

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Selected Publications 📚

• Troumpoukis, A., Klampanos, I. A., Pantazi, D.-A., Albughdadi, M., Baousis, V., Barrilero, O., … Karkaletsis, V. (2024). European AI and EO convergence via a novel community-driven framework for data-intensive innovation. Future Generation Computer Systems, 160, 505-521.
• Karozis, S., Klampanos, I. A., Sfetsos, A., & Vlachogiannis, D. (2023). A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data. Big Earth Data, 7(2), 231–250.
• Davvetas, A., Klampanos, I. A., Skiadopoulos, S., & Karkaletsis, V. (2022). Evidence transfer: Learning improved representations according to external heterogeneous task outcomes. ACM Transactions on Knowledge Discovery from Data , 16(5).
• Politikos, D. V., Fakiris, E., Davvetas, A., Klampanos, I. A., & Papatheodorou, G. (2021). Automatic detection of seafloor marine litter using towed camera images and deep learning. Marine Pollution Bulletin, 164, 111974.
• Klampanos, I. A., Themeli, C., Spinuso, A., Filgueira, R., Atkinson, M., Gemünd, A., & Karkaletsis, V. (2020). DARE platform: A developer-friendly and self-optimising workflows-as-a-service framework for eScience on the cloud. Journal of Open Source Software, 5(54), 2664.
• Klampanos, I. A., Davvetas, A., Gemünd, A., Atkinson, M., Koukourikos, A., Filgueira, R., … Karkaletsis, V. (2019). DARE: A reflective platform designed to enable agile data-driven research on the cloud. In Proceedings of the 15th IEEE International Conference on eScience (pp. 578–585).
• Klampanos, I. A., Davvetas, A., Andronopoulos, S., Pappas, C., Ikonomopoulos, A., & Karkaletsis, V. (2018). Autoencoder-driven weather clustering for source estimation during nuclear events. Environmental Modelling & Software, 102, 84–93.

For a full list of publications, see my Google Scholar profile.

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Teaching 👨‍🏫

2025-2026:

• Introduction to Data Science and Systems(IDSS)
  MSc in Data Science
  Focus: Foundational elements of data science theory and systems, including Data transformations, Database Systems, and practical data processing pipelines.
• Databases and Data Analytics (DDA)
  MSc in Software Development
  Focus: Introduction to Data Management, including databases - relational and NoSQL/Big Data systems, data mining/recommendation systems and large-scale analytics paradigms.

2024-2025:

• Internet Technology (ITECH)
  MSc in Information Technology
  Focus: Web software development.

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Contact 📬