Welcome to the website of the Data and AI cluster at Eindhoven University of Technology!

We study foundations of data and AI for the present and the future. We design new methods, develop algorithms and tools with a view at expanding the reach of databases and AI and their generalization abilities. In particular, we study foundational issues of robustness, safety, fairness, trust, reliability, tractability, scalability, interpretability and explainability of data and AI. Currently, DAI includes five research groups: Uncertainty in AI, Generative AI, Automated ML, Data Mining, and Databases.


DAI research is regularly published at top data and AI journals and conferences such as IJCAI, AAAI, ICML, ECMLPKDD, ICDM, NeurIPS, VLDB, and SIGMOD among others. We actively collaborate with industry through multiple co-funded PhD projects.

Master Projects and Internships

Each academic year DAI attracts 70+ excellent MSc students for their internships and final thesis projects, many of which are done in collaboration with industry and other departments.


DAI is responsible for developing and teaching core data science and AI courses at MSc level, including Foundations of AI, Research Topics in Data Mining, Engineering Data Systems, Deep Learning, Reinforcement Learning, Text Mining, Uncertainty Representation and Reasoning, Generative Models, as well as challenge-based education such as Data Challenges, and at BSc level including Data Mining and Machine Learning, Datamodelling and databases and Responsible Data Science.

Research groups

Joaquin Vanschoren
Automated Machine Learning group

The Automated Machine Learning (AutoML) group explores how to use machine learning to learn how to create better machine learning models. The group focuses on bringing together AutoML, deep learning, meta-learning, transfer learning, continual learning, and other fields towards a single objective.

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Mykola Pechenizkiy
Data Mining group

The data mining (DM) group is known for its contributions in predictive analytics, knowledge discovery and machine learning over evolving data streams. We study how to make machine learning models robust, trustworthy and explainable and how to (self-)audit AI solutions for their compliance to regulations and adherence to responsible AI guidelines, including aspects of privacy, fairness and non-discrimination among others.

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George Fletcher
Database group

The database (DB) group investigates data management and data-intensive systems, inspired by real-world application and analytics scenarios in close cooperation with public sector and industrial research partners. Expertise within the group includes query language design and foundations, query optimization and evaluation, data analytics, and data integration.

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Jakub Tomczak
Generative AI group

The Generative AI group focuses on building deep generative models (probabilistic modeling + deep learning) for defining generative processes, synthesizing new data, and quantifying uncertainty. The research carried out within the group is reinforced by applications in Life Sciences,  Molecular Sciences, signal processing, smart devices, and smart apps (e.g., chatbots, art generation).

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Cassio de Campos
Uncertainty in AI group

The UAI research group at TU/e explores uncertainty in AI and machine learning from multiple angles on principles of AI, theories of representation, probabilistic AI models, algorithms for learning, reasoning and decision making. There is also an important focus on approaches that are not only accurate but efficient, interpretative, robust and trustworthy.

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