Welcome,
Welcome to the website of the Data and AI cluster at Eindhoven University of Technology!
We're hiring!
The Data and AI cluster is hiring!
We currently have room for eight outstanding machine learning Ph.D. students, PostDocs, and Engineers in the Automated Machine Learning group
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.
Research
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.
Courses
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.
Highlights
Research groups
Advanced Models through Open Research and Engineering group
The group on Advanced Models through Open Research and Engineering (AMORE) aims to scientifically understand and build AI systems with advanced capabilities, and make AI accessible to benefit all of humanity. We invent neural network architectures and train them in new ways to learn better and faster. Everything we create is open-source and crafted with user-friendliness in mind.
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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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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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Machine Learning for Physical Sciences group
The Machine Learning for Physical Science group develops advanced computational methods combining Scientific Machine Learning, Deep Generative Modeling, and Geometric Deep Learning to accelerate scientific discovery. They focus on building efficient emulators from synthetic and experimental data, incorporating domain knowledge and physical principles to address challenges in materials science, mechanical engineering, fluid dynamics, and nuclear fusion.
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Scalable Online Data Management group
The SODATA group studies data-intensive systems, focusing on how people and machines can effectively interact with massive, and possibly continuously generated data. We design near-real-time analytics using approximate algorithms, compact data summaries (synopses), and scale-out techniques. Our work provides the foundation for applications ranging from stream analytics to efficient and scalable machine learning.
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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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