Catalogue of Possibilities / Supervisors

Dr Iraklis Klampanos he/him

Intelligent Data Engineering for bridging scientific disciplines and infrastructures

Senior Lecturer of Data Systems and Data Engineering

School of Computing Science

Research interests:

Intelligent systems, Intelligent data engineering, Representation learning, Foundation models, Interdisciplinary research, Scientific infrastructures, Data integration, Knowledge representation, Research data management

Research fields:

Artificial Intelligence, Data engineering, Data-intensive science, Earth observation, Climate, Environmental sciences, AI trustworthiness

Mission Priority Areas

Why do you want to join the DiveIn community?

I would like to contribute to the development of interdisciplinary, data-driven research aligned with national and global priorities. My expertise in Intelligent Data Engineering and my long experience both in applied as well as in fundamental research supports the design of intelligent, scalable, interoperable systems that enable collaboration across scientific domains and infrastructures. The CDT's focus on co-creation, diversity, and mission-driven research aligns well with my areas of expertise and work ethic and inclination. I am particularly interested in contributing to projects in Artificial Intelligence, Big Data, as well as in related interdisciplinary work by supporting the training of promising doctoral researchers.

Personal profile:

My research focuses on Intelligent Data Engineering, with an emphasis on Multimodal AI, intelligent data-intensive systems and platforms that support computational and scientific research. I am passionate about enabling interdisciplinary, data-driven science through scalable, interoperable, and semantically enriched infrastructures.

As Head of the Intelligent Data-Intensive Systems group at NCSR Demokritos (until December 2024) I developed AI methods for data engineering, semantic technologies, and intelligent platforms. My work spanned domains such as Earth Observation, climate science, fusion energy, and environmental modelling, often in collaboration with national and international partners. Now, in Glasgow since January 2025, I am continuing these research directions being active in student supervision, teaching and research proposal preparation.

I actively seek collaborations with domain scientists in physics, environmental sciences, geosciences, and health, aiming to co-develop AI-enhanced methods, data platforms and tools. I have led or contributed to projects with UKAEA, CERFACS, KNMI, INGV and various European consortia as part of research projects.

I am interested in supervising CDT projects that explore AI for scientific data management, explainable multimodal models, and semantic integration across disciplines. I envision projects that combine AI, Big Data, and domain-specific knowledge to address challenges in domains such as climate modelling, environmental research, and Earth observation. I put equal value to cross-cutting requirements such as scientific reproducibility, and AI explainability and trustworthiness.

I provide structured, hands-on supervision with a focus on technical depth, interdisciplinary relevance, and career development. I have been involved in the supervision of 8 PhD students (1 to graduation, currently 2) and over 18 MSc projects, some leading to publications and awards. Graduates have pursued careers in research and industry.

I am committed to fostering an inclusive research environment. My work in cross-disciplinary and international projects has consistently promoted diverse participation, open science, and ethical AI.

Originally trained in the UK, and having had a long experience working and leading European research projects, I bring a European perspective to research and education. On an additional note, I co-founded a fintech startup and enjoy bridging academic research with real-world applications. I value curiosity, collaboration and research integrity.