DEDS

Data Engineering for Data Science Doctoral Network

The Data Engineering for Data Science (DEDS) project is a prestigious European Joint Doctorate program coordinated by the Université libre de Bruxelles (ULB) in collaboration with a consortium of leading international universities and industry partners. Its primary mission is to bridge the gap between data engineering—the infrastructure and pipelines that manage data—and data science, which focuses on extracting insights and value. By fostering research at this intersection, DEDS aims to develop a new generation of researchers capable of managing the entire data lifecycle, from initial capture and storage to sophisticated exploitation and decision-making.

The research within DEDS is structured around a holistic approach to data management, covering key pillars such as data integration, metadata governance, and large-scale processing. Projects within the program utilize advanced technologies like semantic graphs, machine learning, and spatio-temporal modeling to solve complex problems in various domains. For instance, doctoral candidates explore topics like privacy-aware data processing, automated feature selection, and the optimization of data science workloads across distributed computing platforms, ensuring that the resulting systems are both efficient and trustworthy.

A core strength of the DEDS project is its collaborative “co-tutelle” model, where each doctoral student is co-supervised by experts from two different academic institutions. This academic foundation is further bolstered by “secondments,” or temporary placements within partner organizations from the public and private sectors—including industries like energy, finance, health, and transport. This structure ensures that the research remains grounded in real-world practicalities, allowing students to validate their theoretical solutions against actual industrial and societal challenges.

Ultimately, DEDS serves as a hub for innovation and specialized training in the European data landscape. By integrating technical courses with transversal skills and facilitating open science dissemination, the project strengthens Europe’s expertise in the data economy. It addresses the growing demand for highly skilled professionals who can navigate the complexities of “Big Data” while maintaining a focus on transparency, scalability, and the common good.

My role

As supervisor within the DEDS consortium, I lead research focused on the intersection of data engineering and machine learning. I co-supervise four PhD candidates under the MSCA Joint Doctorate framework: Antonios Kontaxakis (ULB/UPC), who is developing frameworks for the optimization of machine learning workflows; Md Ataur Rahman (ULB/UPC), who focuses on semantic-aware heterogeneity management and table-text retrieval; Ikhtiyor Nematov (ULB/AAU), whose work centers on data attribution and explainability for Large Language Models (LLMs); and Daniele Lunghi (ULB/ARC), whose research is on adversarial machine learning and fraud detection.

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