Welcome to the website of the Data and AI cluster. Here you can learn more about who we are and what we do.

For information on us and our research, click Team. If you are interested in graduating within our cluster, find open projects under Master projects. You can also get inspired by currently running or even finished projects. If you want to connect to cluster members for an internship, click Internships.

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 studies data mining techniques and knowledge discovery approaches that are at the core of data science. The group is known for its contributions to the areas of predictive analytics, automation of machine learning and networked science, subgroup discovery and exceptional model mining, and similarity computations on complex data.

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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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