Data and AI cluster

Research group All Advanced Models through Open Research and Engineering Data Mining Database Machine Learning for Physical Sciences Scalable Online Data Management Uncertainty in AI Show items up to All 5 years 10 years 15 years

Master projects

Here you can find all our available master projects.

Open Projects (28)

Empirical Understanding of simple MAP queries in discrete multivariate domains

This project will (empirically) investigate and extend the findings in Almost No News on the Complexity of MAP in Bayesian Networks (https://proceedings.mlr.press/v138/campos20a.html) It requires some coding and desire to run sound and thorough (empirical) analyses.

Cassio de Campos
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Complexity of MAP in simple mixture models

This is a theoretical project that will connectMinimizing Low-Rank Models of High-Order Tensors: Hardness, Span, Tight Relaxation, and Applications (https://ieeexplore.ieee.org/document/10342630)andAlmost No News on the Complexity of MAP in Bayesian Networks (https://proceedings.mlr.press/v138/campos20a.html) generating new theoretical results and fixing imprecisions on existing one. It requires a student …

Cassio de Campos
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Credal models for reasoning shortcut awareness

Reasoning shortcuts can be harmful for AI systems which want to achieve a high level of "understanding" and reasoning capabilities. The XOR MNIST example illustrates well the current situation (see https://arxiv.org/abs/2507.11357 and https://proceedings.mlr.press/v244/marconato24a.html). This project will attempt to use credal models to represent those …

Cassio de Campos
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Beyond monotone circuits

This project will study the representation and learning capabilities of circuits that are not monotone. The basis for the work starts with https://arxiv.org/abs/2310.00724 and https://arxiv.org/abs/2408.00876We will empirically study different types of circuits and attempt to unveil what makes them work (or not).

Cassio de Campos
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Structure learning for multi-dimensional classifiers

This project intends to extend the work in:https://proceedings.mlr.press/v216/nguyen23b/nguyen23b.pdfby adapting ideas for credal structure learning (via optimistic and pessimistic approaches). The theory is being developed by colleagues at UT Compiegne (led by prof VL Nguyen), with whom we will interact to reach the project goals.

Cassio de Campos
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Structure learning of circuits

Structure learning of (probabilistic) circuits is an open problem. This project will attempt to create new ideas and algorithms. Some references:Tractable Uncertainty for Structure Learning: https://arxiv.org/abs/2204.14170Strudel: Learning Structured-Decomposable Probabilistic Circuits: https://arxiv.org/abs/2007.09331Bayesian Structure Scores for Probabilistic Circuits: https://arxiv.org/abs/2302.12130

Cassio de Campos
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Synthetic incomplete data generation

Data generation is an important task, but typically the missing data mechanism is not fully modeled and exploited in the process. This project intends to study such a problem and to create tools for data generation with missing values. Besides data generation from random …

Cassio de Campos
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Offline Model-based Reinforcement Learning

Offline Reinforcement Learning (RL) addresses settings where online interaction is impractical, costly, or unsafe, enabling applications from healthcare to robotics. Learning from offline data is challenging due to distributional shift, which causes extrapolation errors that cannot be corrected without further exploration. Model-free RL methods …

Maryam Tavakol
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Creating Fairness-Aware Datasets for Sequential Decision-Making

As AI systems become more integrated into decision-making across domains such as finance, healthcare, and criminal justice, ensuring fairness has become a key concern. Fairness-aware machine learning (ML) aims to mitigate biases that could lead to discriminatory outcomes, but traditional research often focuses on …

Maryam Tavakol
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Long-term Fairness with Offline Reinforcement Learning

One of the main concerns in the recent AI research is that most data-driven approaches preserve the bias or unfairness available in the collected (offline) data in the resulting models, which could lead to harmful social and ethical effects in the society. Fairness-aware machine learning has …

Maryam Tavakol
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Reinforcement Learning for Efficient Causal Discovery

Understanding causal relationships within data is essential across fields such as healthcare, economics, and social sciences, where knowing "what causes what" guides decision-making and policy. Causal discovery, the process of identifying these relationships and structuring them in causal graphs, remains challenging, especially in complex, …

Devendra Dhami

Maryam Tavakol
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Causal Discovery for Offline Model-Based Reinforcement Learning

Reinforcement Learning (RL) has proven effective in a variety of complex decision-making tasks. However, traditional RL requires extensive online interactions, making it costly and, in some domains, impractical due to constraints on safety, time, or resource availability. Offline RL, which relies solely on pre-collected …

Maryam Tavakol

Devendra Dhami
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Examples of missing not at random (MNAR) data that are difficult for classifiers

It often occurs in datasets that there is missing data. A good introduction can be found here: https://stefvanbuuren.name/fimd/.This missingness might be "completely at random" (MCAR). This occurs when the probability of being missing is the same for all cases. An example of MCAR data …

Arthur van Camp
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Implementation of inference algorithms for the imprecise Plackett–Luce model

The Plackett–Luce model is a popular parametric probabilistic model to define distributions between rankings of objects, modelling for instance observed preferences of users or ranked performances of algorithms. Since such observations may be scarce (users may provide partial preferences, or not all algorithms are …

Arthur van Camp
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Implementing inference algorithms for choice functions

In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …

Arthur van Camp

Cassio de Campos
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Generative Random Forests: The Next Level

The work on generative random forests has started, but there is a long way to make them practical. This project aims at studying the drawbacks of such models and improving them with better ensemble ideas, gradient boosting, and/or other techniques already employed with decision …

Cassio de Campos
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Probabilistic Circuits versus Bayesian networks

This project aims to compare two different types of generative models: tractable probabilistic circuits and Bayesian networks of bounded tree-width, and potentially have tools to translate between them (when possible). Probabilistic circuits have been recently applied to a number of tasks, but there is …

Cassio de Campos
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Hybrid Bayesian networks

This internal project aims at developing and testing (for example in classification tasks) a generative model based on probabilistic graphical models for domains with continuous and categorical variables. We want to learn both the graph structure and parameters of such models while constraining their …

Cassio de Campos
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Synthetic data generation for causal learning

An arguably major difficulty for improving causal inferences is the lack of availability of data. While observational data are abundant, interventional data are not. This internal project aims at creating software tools to generate data that can be useful for testing causal learning approaches. …

Cassio de Campos
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Scalable Implementation of Probabilistic Circuits

This internal project aims at designing and development a usable software package for learning and reasoning with probabilistic circuits. Probabilistic circuits are models which can represent complicated mixture models and their computation circuit can be wide and deep. Because they have a structure which …

Cassio de Campos
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Algorithms for forward irrelevance with choice functions

In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …

Arthur van Camp

Cassio de Campos
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Concepts of independence for choice functions

In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …

Arthur van Camp

Cassio de Campos
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Local inference algorithms for choice functions

In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …

Arthur van Camp

Cassio de Campos
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Interventional Whittle Sum-Product Networks

Whittle sum-product networks [1] model the joint distribution of multivariate time series by leveraging the Whittle approximation, casting the likelihood in the frequency domain, and place a complex-valued sum-product network over the frequencies. The conditional independence relations among the time series can then be …

Devendra Dhami
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Dynamic Knowledge Graph Embeddings

Knowledge graph embeddings are an important area of research inside machine learning and has become a necessity due to the importance of reasoning about objects, their attributes and relations in large graphs. There have been several approaches that have been explored and can be …

Devendra Dhami
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Computational Complexity of Probabilistic Circuits

This internal project aims at studying and devising new bounds for the computational complexity of inferences in probabilistic circuits and their robust/credal counterpart, including approximation results and fixed-parameter tractability. It requires mathematical interest and good knowledge of theory of computation. This is a theoretical …

Cassio de Campos
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Learning Bayesian Networks in a Single Step

This internal project aims at implementing a new approach to learning the structure and parameters of Bayesian networks. It is mostly an implementation project, as the novel ideas are already established (but never published, so the approach is novel). It requires high expertise in …

Cassio de Campos
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Battle of the credal networks: strong independence or forward irrelevance?

Bayesian networks are a popular model in AI. Credal networks are a robust version of Bayesian networks created by replacing the conditional probability mass functions describing the nodes by conditional credal sets (sets of probability mass functions). Next to their nodes, Bayesian networks are …

Erik Quaeghebeur
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Assigned Projects (5)

Finished Projects (43)

On Neural Cellular AutomataJul 2024

The design of collective intelligence, i.e. the ability of a group of simple agents to collectively cooperate towards a unifying goal, is a growing area of machine learning research aimed at solving complex tasks through emergent computation [1, 2]. The interest in these techniques …

Erik Quaeghebeur

Gennaro Gala
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Efficient surrogates for the Ainslie wind turbine wake modelAug 2023

In wind farms, one source of reduction in power generation by the turbines is the reduction of wind speed in the wake downstream of each turbine's rotor. Namely, a turbine downstream in the wind direction of another will effectively experience wind with a reduced …

Erik Quaeghebeur

LB
Laurens Bliek (Information Systems, IE&IS)
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Identifying robust instances in classificationJul 2023

In a classification task, some instances are classified more robustly than others. Namely, even with a large modification of the training set, these instances (in the test set) will be assigned to the same class. Other instances are non-robust in the sense that a …

Erik Quaeghebeur
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Filter Research group All Advanced Models through Open Research and Engineering Data Mining Database Machine Learning for Physical Sciences Scalable Online Data Management Uncertainty in AI Show items up to All 5 years 10 years 15 years

Master projects

Open Projects (28)

Empirical Understanding of simple MAP queries in discrete multivariate domains

Complexity of MAP in simple mixture models

Credal models for reasoning shortcut awareness

Beyond monotone circuits

Structure learning for multi-dimensional classifiers

Structure learning of circuits

Synthetic incomplete data generation

Offline Model-based Reinforcement Learning

Creating Fairness-Aware Datasets for Sequential Decision-Making

Long-term Fairness with Offline Reinforcement Learning

Reinforcement Learning for Efficient Causal Discovery

Causal Discovery for Offline Model-Based Reinforcement Learning

Examples of missing not at random (MNAR) data that are difficult for classifiers

Implementation of inference algorithms for the imprecise Plackett–Luce model

Implementing inference algorithms for choice functions

Generative Random Forests: The Next Level

Probabilistic Circuits versus Bayesian networks

Hybrid Bayesian networks

Synthetic data generation for causal learning

Scalable Implementation of Probabilistic Circuits

Algorithms for forward irrelevance with choice functions

Concepts of independence for choice functions

Local inference algorithms for choice functions

Interventional Whittle Sum-Product Networks

Dynamic Knowledge Graph Embeddings

Computational Complexity of Probabilistic Circuits

Learning Bayesian Networks in a Single Step

Battle of the credal networks: strong independence or forward irrelevance?

Assigned Projects (5)

Context-Aware Model-Based Offline Reinforcement LearningJan 2026

Building a drought impact database for the Netherlands and cross-border catchmentsAug 2025

Autonomous Apple Harvesting via Reinforcement LearningJun 2025

Breeding Program Optimization via Offline Reinforcement LearningApr 2025

Uncertainty Estimation in Model-Based Offline Reinforcement Learning using Random NetworksApr 2025

Finished Projects (43)

On Neural Cellular AutomataJul 2024

Efficient surrogates for the Ainslie wind turbine wake modelAug 2023

Identifying robust instances in classificationJul 2023

Research group All Advanced Models through Open Research and Engineering Data Mining Database Machine Learning for Physical Sciences Scalable Online Data Management Uncertainty in AI Show items up to All 5 years 10 years 15 years