Master projects
Here you can find all our available master projects.
Open Projects (58)
-
Uncertainty Quantification via Test Time Augmentation
Test-Time Augmentation (TTA) refers to applying data augmentations not only during training, but also during inference. For a single input, such as an image, the model is evaluated on multiple augmented versions of that input, for example rotated or cropped variants. The final prediction …
Sibylle Hess
More info
Fabian Denoodt
-
AI‑Driven Legal Search with Knowledge Graphs for Dutch Jurisprudence
[Please note that this project is primarily aimed at Dutch-speaking students, as it requires working with large amounts of Dutch texts.]AI-powered search has rapidly become the new standard for search engines. However, popular techniques (e.g. AI agents or retrieval-augmented generation), face fundamental limitations when …
More info
Hilde Weerts
-
Adaptive Inference Time in Neural Networks with Early-exit Neural Networks & Uncertainty Quantification
Increasing a neural network's architecture size can significantly improve performance, but it also makes inference slow and resource-intensive. Early-exit neural networks (EENNs) address this by adding extra classifiers at intermediate layers, so that easy inputs can exit early and save computation. The key question …
Sibylle Hess
More info
Fabian Denoodt
-
(External: PwC) "Where does this number come from?": Enterprise Data Lineage using LLMs and Knowledge Graphs
In Financial and ESG reporting, we see that it takes a lot of time to create insights into data lineage of the reports that companies publish on their Financial, Environmental, Social and Governmental related KPIs. Quality, transparency and integrity would be supported if we …
More info
Bart Engelen
-
(External: PwC) Optimization and Pareto fronts in the Responsible Business Simulator
Introduction In the current business landscape, companies are balancing profitability and social responsibility. The Responsible Business Simulator (RBS) is a powerful tool designed by PwC to help businesses make responsible strategic choices by simulating the impact of various decisions on diverse outputs. This master's …
More info
Bart Engelen
-
(External: PwC) Write your thesis at PwC Advisory Data Analytics
Do you want to write your master's thesis about a Data & AI related topic on real-world client cases? We offer you the opportunity to write your thesis within PwC's Data Analytics Advisory team. This is a multidisciplinary team that uses its analytical skills …
More info
Bart Engelen
-
Towards Meaningful Metrics for Monosemanticity and Disentanglement
Recent work in representation learning—especially in interpretability research—frequently refers to monosemanticity: the idea that individual units (neurons, features, or directions in representation space) correspond to a single, well-defined concept. Closely related is the notion of disentanglement, where different latent dimensions are expected to capture …
Sibylle Hess
More info
Surja Chaudhuri
-
Semantic Hashing of the Epstein Files
Large investigative document releases often contain tens of thousands of heterogeneous files: transcripts, motions, scanned exhibits, emails, duplicates, partially redacted documents, and large amounts of procedural boilerplate. In practice, sheer volume can become a filtering mechanism. When everything is available, nothing is easily accessible. …
More info
Sibylle Hess
-
A Study on Decomposition-Based Forecasting Models: Does Time Series Decomposition really Help?
Background & MotivationMany recent time series forecasting models (e.g., ETSformer[1], Autoformer[2], FEDformer[3], SCINet[4], DLinear [5]) incorporate series decomposition into trend and seasonal components before prediction. The intuition is that separating these underlying patterns makes forecasting easier and more accurate.However, decomposition does not always help …
More info
Amy Deng
-
A Study of Non-Stationary Time Series Forecasting: Analysis, Limitations, and a New Adaptive Framework
Background & MotivationReal-world time series often exhibit non-stationarity, where statistical properties such as mean, variance, or seasonal structure evolve over time. This poses a major challenge for accurate and robust forecasting. Recent methods like RevIN (Reversible Instance Normalization) [1], SAN (Slicing Adaptive Normalization) [2], FAN (Frequency Adaptive …
More info
Amy Deng
-
Using Privilled Information in Sequential Decision Making with Partial Observations
Reinforcement Learning (RL) (Sutton and Barto, 2018) addresses problems that can be modeled as a Markov decision process (MDP) (Puterman, 1994), where the transition function is unknown. In situations where an arbitrary policy is already in execution and the experiences with the environment were …
Thiago Simão
More info
Maryam Tavakol
-
[Internship at TeleOperation]Enhancing Real-World Imitation Learning with Reinforcement Learning
This TU/e master project is setup in collaboration with a robotics start-up in Eindhoven. Applications are now open and will remain open until a suitable candidate is found (details below).Company OverviewTeleOperation Services is an innovative company based in Woensel-Noord, Eindhoven. Our cutting-edge AI-driven system …
Thiago Simão
More info
Bram Grooten
-
Race Car Tuning as Configurable Reinforcement Learning
Deep reinforcement learning has been successfully used for driving a car in the Gran Turismo video game, outperforming experts (Wurman et al., 2022). However, an open question remains: how to tune the car used during the races?This problem can be modeled as a configurable …
More info
Thiago Simão
-
Part replacement identification using Knowledge Graphs
(This project is also available as an internship)Company: Marel Location: Boxmeer BackgroundIt is important for industrial equipment developers to provide accurate part replacements to their customers. Parts can wear over time or break and having suitable replacements is a dynamic process based on availability, …
Mykola Pechenizkiy
More info
Zeno van Cauter
-
Alternative latent space models for vessel re-identification
— not available anymore —Coastal surveillance cameras are often used to detect (distinguish from the background) and recognize (as belonging to a class) non-cooperative vessels, i.e. vessels not reporting their position and identity using an AIS [1] transponder through a TDMA network such that …
Mykola Pechenizkiy
More info
Stiven Schwanz Dias
-
Vision-centric image tokenization in the generative transformer era
Generative autoregressive next token prediction has shown impressive success in LLMs. Several works have attempted to extend the success of LLMs to vision-language tasks with VLMs. While a VLM can be designed specifically for image-to-text tasks like visual question answering, many works also attempt …
Bahram Zonooz
More info
Elahe Arani
-
Maritime traffic anomaly detection
— not available anymore —Coastal surveillance systems rely on multiple sensors to perform object assessment [1], i.e., to detect and track the sequence of vessels' states including their position and velocity (where are the vessels at a given timestamp?). In general, surface radars are …
Mykola Pechenizkiy
More info
Stiven Schwanz Dias
-
Leveraging Language Semantics for Enhanced Understanding and Generalization
The field of artificial intelligence has seen unprecedented growth in recent years, particularly with the advent of foundation models and large language models (LLMs). These models have showcased remarkable capabilities across a broad spectrum of applications, including natural language processing and multimodal tasks. Traditionally, …
Bahram Zonooz
More info
Elahe Arani
-
Architectural Analysis of Vision Transformers in Continual Learning
Deep neural networks (DNN) deployed in the real world are frequently exposed to non-stationary data distributions and required to sequentially learn multiple tasks. This requires that DNNs acquire new knowledge while retaining previously obtained knowledge and this is imperative in applications like autonomous driving …
Bahram Zonooz
More info
Elahe Arani
-
True continual learners in the wild: CL beyond artificial constraints / datasets
Continual Learning (CL) is a learning paradigm in which computational systems progressively acquire multiple tasks as new data becomes available over time. An effective CL system must find a balance between being adaptable to integrate new information and maintaining stability to prevent disruption of …
Bahram Zonooz
More info
Elahe Arani
-
Exploring sparsity in lifelong learning
In the dynamic world, deep neural networks (DNNs) must continually adapt to new data and environments. Unlike humans, who can learn continually without forgetting past knowledge, DNNs often suffer from catastrophic forgetting when exposed to new data, causing them to lose previously acquired information. …
Bahram Zonooz
More info
Elahe Arani
-
Image representation learning in autoregressive transformers
With the recent success of LLMs, and the strong potential of multi-modal learning from both text and vision, several works have framed images as sequences to conform with generative sequence-to-sequence encoder-decoder or decoder based transformers [1]. Such formulations present advantages such as unified architectures …
Bahram Zonooz
More info
Elahe Arani
-
AI for 3D Concrete Printing
— not available anymore —Designing 3D printable materials has been, so far, a trial-and-error process dependent on human knowledge and effort; hence time-consuming and wasteful. To predict certain properties of 3DCP, material scientists have used modelling and simulations for decades. While helpful in many …
Mykola Pechenizkiy
More infoSBSandra Lucas and Önder Babur -
Counterfactual explanations
I plan to offer a few assignments on counterfactual explanationsCounterfactual explanations on evolving dataFeasibility, actionability and personalization of counterfactual explanationsCounterfactual explanations for spotting unwanted biased in predictive model behaviourValue alignment for counterfactual explanations (in collaboration with Emily Sullivan)Counterfactual explanations for behaviour change
More info
Mykola Pechenizkiy
-
Enhancing Reconstructive Surgery Decision-Making
The goal of this project would be to come up with a transformer or any other smart solution to (in a one sentence oversimplified description) find mappings between an image of the current patient condition, possible surgery actions and preferred outcome image. A more detailed …
More info
Mykola Pechenizkiy
-
Preventing Beam Pollution: Defining an Empirical Protocol to Improve Beam Search Lattice Traversal
See PDF
More info
Wouter Duivesteijn
-
Safe reinforcement learning with decision transformers
Safety is a core challenge for the deployment of reinforcement learning (RL) in real-world applications [1]. In applications such as recommender systems, this means the agent should respect budget constraints [2]. In this case, the RL agent must compute a policy condition of the …
More info
Thiago Simão
-
Learning Decision Trees to Reduce the Sample Complexity of Offline Reinforcement Learning
Reinforcement Learning (RL) deals with problems that can be modeled as a Markov decision process (MDP) where the transition function is unknown. When an arbitrary policy was already in execution, and the experiences with the environment were recorded in a dataset, an offline RL …
More info
Thiago Simão
-
Reinforcement Learning for Configurable Systems
Nowadays, most software systems are configurable, meaning that we can tailor the settings to the specific needs of each user. Furthermore, we may already have some data available indicating each user's preferences and the software's performance under each configuration. This way, we can compute …
More info
Thiago Simão
-
Deriving Valuation Bases to Expand GP-Growth to More EMM Model Classes
See PDF
More info
Wouter Duivesteijn
-
Surveying to Bring Order in the Jungle of Supervised Local Pattern Mining Implementations
See PDF
More info
Wouter Duivesteijn
-
Expanding Exceptional Model Mining on Unstructured Data
See PDF. As attachment, see also https://wwwis.win.tue.nl/~wouter/MSc/Niels.pdf
More info
Wouter Duivesteijn
-
Finding the Curse of Dimensionality Sweet Spot Between Traditional Clustering and Deep Clustering
See PDF
Wouter Duivesteijn
More info
Sibylle Hess
-
Pimp my BUS: Improving a Minicluster-based Deterministic Pattern Sampling Algorithm for Exceptional Model Mining
See PDF. As attachment, see also https://wwwis.win.tue.nl/~wouter/MSc/Bart.pdf
More info
Wouter Duivesteijn
-
Topics in Deep Clustering
Deep clustering is a well-researched field with promising approaches. Traditional nonconvex clustering methods require the definition of a kernel matrix, whose parameters vastly influence the result, and are hence difficult to specify. In turn, the promise of deep clustering is that a feature transformation …
More info
Sibylle Hess
-
Peer-to-peer Federated Learning
--update--: This project is now taken by Davis EisaksThe goal of this project is to study how to train a machine learning model in a gossip-based approach, where if two devices (e.g smartwatches) pass each other in the physical space, they could exchange part of …
Mykola Pechenizkiy
More infoTDTim d'Hondt -
Bayesian Federated Learning using node-based BNNs.
Node-based BNNs assign latent noise variables to hidden nodes of a neural network. By restricting inference to the node-based latent variables, node stochasticity greatly reduces the dimension of the posterior. This allows for inference of BNNs that are cheap to compute and to communicate, …
Mykola Pechenizkiy
More infoTDTim d'Hondt -
ML projects at ASML
ASML has recently re-confirmed there two projects; a couple more will likely be confirmed in the coming weeksXAI in Exceptional Model Mining (--- update --- this project is taken by Yasemin Yasarol)In the semiconductor industry there are different, diverse and unique failure modes that impact …
Mykola Pechenizkiy
More infoTTtbc -
ML projects at Floryn
--- update --- These projects are no longer available. Theonymfi Anogeianaki will work on FairML.1. Bayesian inferenceWe have been doing ‘traditional’ machine learning for years now at Floryn but never investigated Bayesian modeling. We currently make use of probability measures that come from our (frequentist) machine learning …
More info
Mykola Pechenizkiy
-
Continual Structure from Motion
Autonomous vehicles and robots need 3D information such as depth and pose to traverse paths safely and correctly. Classical methods utilize hand-crafted features that can potentially fail in challenging scenarios, such as those with low texture [1]. Although neural networks can be trained on …
Bahram Zonooz
More info
Elahe Arani
-
Diversifying attention through randomization in sparse neural networks training
Context of the work: Deep Learning (DL) is a very important machine learning area nowadays and it has proven to be a successful tool for all machine learning paradigms, i.e., supervised learning, unsupervised learning, and reinforcement learning. Still, the scalability of DL models is …
Mykola Pechenizkiy
More info
Ghada Sokar
-
Topics in Continual Lifelong Learning
Nowadays, data changes very rapidly. Every day new trends appear on social media with millions of images. New topics rapidly emerge from the huge number of videos uploaded on Youtube. Attention to continual lifelong learning has recently increased to cope with this rapid data …
Mykola Pechenizkiy
More info
Ghada Sokar
-
Multi-modal Representation Learning and Applications
With the rapid development of multi-media social network platforms, e.g., Instagram, Tiktok, etc., more and more content is generated in the multi-modal format rather than pure text. This brings new challenges for researchers to analyze the user generated content and solve some concrete problems …
Yulong Pei
More info
Tianjin Huang
-
Architectural Analysis of Vision Transformers in Continual Learning
Deep neural networks (DNN) deployed in the real world are frequently exposed to non-stationary data distributions and required to sequentially learn multiple tasks. This requires that DNNs acquire new knowledge while retaining previously obtained knowledge. However, continual learning in DNNs, in which networks are …
Elahe Arani
More info
Bahram Zonooz
-
Human Visual System Inspired Mechanisms for Data Curation
Every second, around 107 to 108 bits of information reach the human visual system (HVS) [IK01]. Because biological hardware has limited computational capacity, complete processing of massive sensory information would be impossible. The HVS has therefore developed two mechanisms, foveation and fixation, that preserve perceptual performance …
Bahram Zonooz
More info
Elahe Arani
-
Human Visual System Inspired Mechanisms for Interpretability
Every second, around 107 to 108 bits of information reach the human visual system (HVS) [IK01]. Because biological hardware has limited computational capacity, complete processing of massive sensory information would be impossible. The HVS has therefore developed two mechanisms, foveation and fixation, that preserve perceptual performance …
Elahe Arani
More info
Bahram Zonooz
-
Human Visual System Inspired Mechanisms for Video Action Recognition/Prediction
Every second, around 107 to 108 bits of information reach the human visual system (HVS) [IK01]. Because biological hardware has limited computational capacity, complete processing of massive sensory information would be impossible. The HVS has therefore developed two mechanisms, foveation and fixation, that preserve perceptual …
Bahram Zonooz
More info
Elahe Arani
-
Online knowledge distillation for self-supervised learning
Self-supervised learning [1, 2] solves pretext prediction tasks that do not require annotations in order to learn feature representations. Recent empirical research has demonstrated that deeper and wider models benefit more from task-agnostic use of unlabeled data than their smaller counterparts; i.e., smaller models …
Bahram Zonooz
More info
Elahe Arani
-
Deep Clustering: Simultaneous Optimization of Representations and Clustering
Deep clustering is a well-researched field with promising approaches. Traditional nonconvex clustering methods require the definition of a kernel matrix, whose parameters vastly influence the result, and are hence difficult to specify. In turn, the promise of deep clustering is that a feature transformation …
More info
Sibylle Hess
-
Fairness-aware Influence Minimization for Combating Fake News
— completed —Influence blocking and fake news mitigation have been the main research direction for the network science and data mining research communities in the past few years. Several methods have been proposed in this direction [1]. However, none of the proposed solutions has …
Mykola Pechenizkiy
More info
Akrati Saxena
-
Towards Cognitive-inspired Adversarial Training Approach
Deep neural networks (DNN) are achieving superior performance in perception tasks; however, they are still riddled with fundamental shortcomings. There are still core questions about what the network is truly learning. DNNs have been shown to rely on local texture information to make decisions, …
Elahe Arani
More info
Bahram Zonooz
-
Generalizable, fair and explainable default predictors
— not available anymore —Context:Financial sector is a tightly regulated environment. All models used in the financial sector, are studied under the microscope of developers, validators, regulators, and eventually the end users – the clients, before these models can be deployed and used.To assess whether …
Mykola Pechenizkiy
More infoDDDLL -
Input Adaptive Inference for Semantic Segmentation
Neural networks typically consist of a sequence of well-defined computational blocks that are executed one after the other to obtain an inference for an input image. After the neural network has been trained, a static inference graph comprising these computational blocks is executed for …
Bahram Zonooz
More info
Elahe Arani
-
Robust symbol detection and recognition in piping and instrumentation diagrams
— not available anymore —Project description This project is concerned with the recognition of symbols of piping and process equipment together with the instrumentation and control devices that appear on piping and instrumentation diagrams (P&ID). Each item on the P&ID is associated with a pipeline. Piping …
Mykola Pechenizkiy
More info
Stiven Schwanz Dias
-
Fairness Analysis in Anomaly Detection
In anomaly detection, we aim to identify unusual instances in different applications, including malicious users detection in OSNs, fraud detection, and suspicious bank transaction detection. Most of the proposed anomaly detection methods are dependent on network structure as some specific structural pattern can convey …
Mykola Pechenizkiy
More info
Akrati Saxena
-
Curiosity driven fairness in Reinforcement learning
Reinforcement learning (RL) is a computational approach to automating goal-directed decision making. In this project, we will use the framework of Markov decision processes. Fairness in reinforcement learning [1] deals with removing bias from the decisions made by the algorithms. Bias or discrimination in …
Mykola Pechenizkiy
More info
Pratik Gajane
-
Generate fair (pseudo) samples for reinforcement learning
Reinforcement learning (RL) is a computational approach to automating goal-directed decision making. Reinforcement learning problems use either the framework of multi-armed bandits or Markov decision processes (or their variants). In some cases, RL solutions are sample inefficient and costly. To address this issue, some …
Mykola Pechenizkiy
More info
Pratik Gajane
-
Causal perspective of fairness in reinforcement learning
Reinforcement learning (RL) is a computational approach to automating goal-directed decision making using the feedback observed by the learning agent. In this project, we will be using the framework of multi-armed bandits and Markov decision processes. Observational data collected from real-world systems can mostly …
Mykola Pechenizkiy
More info
Pratik Gajane
Assigned Projects (24)
-
Your neural network is secretly wasting capacity: why (not) compress it?Mar 2026
Please note this project is no longer available. We train ever larger and larger neural networks. However, several studies indicate that large parts of those large networks are not actually contributing to their performance. It has for example been shown that some layers and …
More info
Hannah Pinson
-
Deep learning for solving PDEsMar 2026
Partial Differential Equations (PDEs) are the backbone of modern science and engineering, governing phenomena from climate modeling and drug discovery to aerospace design and seismic imaging. Solving these equations with classical numerical methods can be computationally very intensive. More recently, numerous neural network methods …
Hannah Pinson
More infoPAProf. Dr. Victorita Dolean-Maini (scientific computing) and Dr. Michael Abdelmalik (computational fluid dynamics) -
Deriving Upper Confidence Bounds to Expand Monte-Carlo Tree Search to More EMM Model ClassesMar 2026
See PDF
HQHongda QiMore info
Wouter Duivesteijn
-
Generating Missing At Random (MAR) data in Images; a Convolutional ApproachFeb 2026
See PDF
DWDavid Wolf
Wouter Duivesteijn
More info
Vlado Menkovski
-
Exceptional Gestalt Mining (EGM)Feb 2026
See PDF
KGKonstantin Georgiev
Wouter Duivesteijn
More infoTDThomas van Dijk -
Reinforcement Learning under Dynamic Safety BoundsNov 2025
Safe Reinforcement Learning (Safe RL) typically assumes a fixed safety bound or cost threshold, constraining the agent’s behavior during training and evaluation. However, in many real-world applications (e.g., robotics, autonomous driving, or healthcare), safety requirements are not static. Regulatory limits, environmental constraints, or critical …
SGSarthak Goswami
Tristan Tomilin
More info
Thiago Simão
-
[Internship at TNO] Fair Resource Allocation in Car-free AreasNov 2025
Applications are now open and will remain open until a suitable candidate is found (details below). The XCARCITY program investigates how to facilitate and support the implementation of car-free areas in Amsterdam, Almere Pampus, and the Metropoolregio Rotterdam Den Haag.Car-free and car-low areas offer …
LRLorenzo RotaMore info
Thiago Simão
-
MedGPT – LLM/foundation models in healthcare & patient privacy (multiple projects)Oct 2025
Please note these projects are no longer availableWe have multiple graduation projects available within the context of the MedGPT project, a large European project focusing on the safe and ethical use of LLMs and foundation models in healthcare. For students interested in gaining a …
TTtMore info
Hannah Pinson
-
Reinforcement Learning with Many ConstraintsOct 2025
In this project, we study the development of reinforcement learning (RL) for applications with many constraints.Applying RL requires designing the reward function, which can be challenging in applications with many objectives. For instance, in autonomous driving, the RL agent should minimize the time to …
APAndrei PopoviciuMore info
Thiago Simão
-
Procedural 3D Environment Generation for Image-Based Reinforcement LearningSep 2025
As autonomous systems evolve, static simulation environments for training reinforcement learning agents increasingly fail to prepare algorithms for real-world variability. Procedural content generation (PCG) [5] in 3D environments offers a low-cost solution to automatically creating a near-infinite variety of dynamic training scenarios. This has the …
GHGiuseppe Hannen
Tristan Tomilin
More info
Thiago Simão
-
Multimodal Time Series Analysis for Out-of-Distribution GeneralizationSep 2025
Introduction Multimodal time series analysis is an emerging field that leverages multiple data sources to enhance predictive modeling. In retail, time series data often includes numerical sales records and image-based product information, providing a rich dataset for forecasting demand and understanding customer behavior. However, …
RRRaneeMore info
Amy Deng
-
Learning to Adapt: Test-Time Normalization for Non-Stationary Time Series ForecastingSep 2025
Background & Motivation:In real-world time series forecasting tasks—such as energy demand, traffic, or financial signals, data distributions often shift over time. These non-stationarities (e.g., changes in trend, seasonality, or noise) can significantly degrade model performance at test time.Recent methods like RevIN (Reversible Instance Normalization) …
NWNaijia WanMore info
Amy Deng
-
A Matrix Factorization approach to Exceptional Model MiningAug 2025
Exceptional Model Mining aims to identify subgroups in the dataset that behave somehow exceptionally. It differs from a clustering approach since subgroups may overlap; not all data points are assigned to a cluster. However, consequently, the list of subgroups often contains many similar, redundant …
GKGheytuli, Kasra
Rianne Schouten
More info
Sibylle Hess
-
Benchmarking a Multimodal Time Series DatasetJun 2025
1. IntroductionMultimodal time series datasets are increasingly valuable in finance, healthcare, industrial monitoring, and other domains. However, their availability remains limited, and standardized benchmarking is underexplored. This project benchmarks a new multimodal time series dataset from the company WAIR, assessing its unique characteristics and …
ZTZhang TianyiMore info
Amy Deng
-
Safe Contrastive Imitation LearningApr 2025
MotivationIn safety-critical domains such as autonomous driving, healthcare robotics, and industrial automation, it is imperative for autonomous agents to not only perform tasks efficiently but also safely. Traditional imitation learning enables agents to learn behaviors by mimicking expert demonstrations. However, these methods often overlook …
ACAnnika Möller Chandiramani
Tristan Tomilin
More info
Thiago Simão
-
Mining Causal Relations Between Real-world Human EventsMar 2025
Motivation: The ACLED dataset (https://acleddata.com/knowledge-base/codebook/) provides a detailed record of political violence and protest events, capturing actors, timelines, and descriptions of the incidents. However, this rich data remains largely underutilized when it comes to understanding the causal relationships between events. While common knowledge graphs typically rely …
TLTianyuan LiuMore info
Amy Deng
-
[Internship at TNO] Dynamic road space allocation with shared mobility hubsNov 2024
The XCARCITY project investigates how to facilitate and support implementation of car-free areas in Amsterdam, Almere Pampus and Metropoolregio Rotterdam Den Haag.Car-free and car-low areas offer many benefits by freeing up road space, reducing congestion and parking requirements, and generally contributing to increased livability …
DVDido Verstegen
Thiago Simão
More infoCPCanmanie T. Ponnambalam -
Reinforcement Learning for Contact-rich and Impact-aware Robotic TasksNov 2024
Reinforcement Learning (RL) [6] has achieved successful outcomes in multiple applications, including robotics [1]. A key challenge to deploying RL in such a scenario is to ensure the agent is robust so it does not lose performance even if the environment's geometry and dynamics …
More info
Thiago Simão
-
Dual arm manipulation of heavy objects with humanoid robots via reinforcement learningNov 2024
Motivation. Reinforcement Learning(RL; Sutton and Barto 2018) has achieved successful outcomes in multiple applications, including robotics(Kober, Bagnell, and Peters 2013). A key challenge to deploying RL in such a scenario is to ensure the agent is robust so it does not lose performance even …
More info
Thiago Simão
-
Multi-agent reinforcement learning for sustainable touristic recommender systemNov 2024
A touristic recommender system (TRS; Dalla Vecchia et al., 2024; Gaonkar et al., 2018; de Nijs et al., 2018) often provides to its users a sequence of recommendations instead of a single suggestion to optimize the user experience in the available time interval. Due …
PDPaul Dewez
Thiago Simão
More infoEQElisa Quintarelli -
High-performance Safe RL BenchmarkingNov 2024
As AI systems become increasingly integral to critical sectors, ensuring their safety and reliability is essential. Reinforcement Learning (RL) is a prominent method that learns optimal behaviors through trial-and-error interactions with a dynamic environment. Yet, the stakes are high: in physical settings, a wrong …
MBMourad Boustani
Tristan Tomilin
More info
Thiago Simão
-
Adversarial attacks for safe transfer in reinforcement learningNov 2024
Safety is a paramount challenge for the deployment of autonomous agents. In particular, ensuring safety while an agent is still learning may require considerable prior knowledge (Carr et al., 2023; Simão et al., 2021). A workaround is to pre-train the agent in a similar …
CMCheuk Lam MoMore info
Thiago Simão
-
Understanding deep learning: efficient retraining of networksJul 2024
Recent work has shown that neural networks, such as fully connected networks and CNNs, learn to distinguish between classes from broader to finer distinctions between those classes [1,2] (see Fig. 1). Figure 1: Illustration of the evolution of learning from broader to finer distinctions between …
More info
Hannah Pinson
-
Understanding deep learning: the initial learning rateJul 2024
This project is finished/closed. While deep learning has become extremely important in industry and society, neural networks are often considered ‘black boxes’, i.e., it is often believed that it is impossible to understand how neural networks really work. However, there are a lot of …
More info
Hannah Pinson
Finished Projects (43)
-
AI for histopathology of melanomaFeb 2026
BackgroundMelanoma is a form of skin cancer that originates in melanin-producing cells known as melanocytes. While other skin cancer types occur more frequently, melanoma is most dangerous due to the high likelihood of metastasis if not treated early. The incidence rate of melanoma has …
Sibylle Hess
More infoMVMitko Veta -
Exceptional Gestalt Mining (EGM)Feb 2026
See PDF
PRPim Rietjens
Wouter Duivesteijn
More infoTBThomas C. van Dijk, Ruhr-Universität Bochum -
[Closed] (PwC) LLMs for Data Analysis PipelinesJan 2026
LLM has the potential to make data more accessible to a non-technical audience through prompt-based analytics. It also has the potential to help make engineering teams more efficient by quickly getting a first draft of a data pipeline.Both of these applications hinge on appropriate …
More info
Bart Engelen
-
[Closed] (PwC) Enhancing Anomaly Detection: Integrating User ExplainabilityDec 2025
PwC developed an unsupervised Transformer-based anomaly detection tool to enhance insights into machine functionality in factories by analyzing machinery timeseries sensor data. However, the current solution lacks explainability for why certain time windows are flagged as anomalous. Root cause algorithms, such as Bayesian inference, …
More info
Bart Engelen
-
[Closed] (PwC) Question bank generator for Applied GenAIDec 2025
(PwC) Question bank generator for Applied GenAI PwC has developed several GenAI applications using models that have been trained on a large corpus of text and can retrieve relevant parts of that corpus when prompted by a user's questions (known as RAG-LLMs). Though many …
More info
Bart Engelen
-
Physics Informed AI for improved cancer prognosisAug 2025
In order to metastasize, cancer cells need to move. Estimating the ability for cells to move, i.e. their dynamics, or so-called migration potential, is a promising new indicator for cancer patient prognosis (overall survival) and response to therapy. However, predicting the migration potential from …
BMBram Meijer
Sibylle Hess
More infoSVSecondary supervisors could be Liesbeth Janssen or Mitko Vetka. -
Leveraging LLMs to create multimodal explanationsAug 2025
AI is currently being used in a wide range of applications. However, most AI systems operate as a black box, meaning that it is hard to understand how an AI system comes to its predictions. Explainable AI (XAI) is a research field that tries …
Sibylle Hess
More infoWMWil Michiels -
Understanding deep learning – exploring the development of complexity in neural networks over depth and timeMay 2025
Introduction: When we train deep, nonlinear neural networks, we often assume that the applied transformations at every layer are effectively nonlinear. Earlier work (Kalimeris et al., 2019)has shown that in the beginning of training, the complete function that deep, nonlinear networks implement is close …
Hannah Pinson
More info
Aurélien Boland
-
Language Agents for Playing Card GamesDec 2024
The project is a pioneering initiative that combines Natural Language Processing (NLP) and Reinforcement Learning (RL) methodologies to create intelligent agents capable of understanding natural language instructions and participating in playing card games. This project aims to develop AI-driven agents that not only comprehend …
Meng Fang
More info
Yudi Zhang
-
Playing Text-based Games with Large Language ModelsDec 2024
The project aims to explore the utilization of sophisticated language models in the domain of text-based games. This endeavor seeks to harness the capabilities of large language models, such as GPT (Generative Pre-trained Transformer), in the context of interactive narratives, text adventures, and other …
Meng Fang
More info
Yudi Zhang
-
Multi-Agent Reinforcement Learning for Cooperative TasksDec 2024
Multi-Agent Reinforcement Learning (MARL) is a field in artificial intelligence where multiple agents learn to make decisions in an environment through reinforcement learning. In the context of cooperative tasks, it involves agents working together to achieve common goals, sharing information and coordinating their actions …
LBLuka van den Boogaard
Meng Fang
More info
Tristan Tomilin
-
Large language Model Based Chatbots and their ApplicationsDec 2024
In recent years, large language models have revolutionized how machines understand and generate human-like text, offering profound implications for chatbot technology. This thesis proposes a deep exploration into the capabilities of these models within chatbot applications, aiming to enhance how they mimic human conversational …
Meng Fang
More info
Jiaxu Zhao
-
Curriculum Learning for Constrained Reinforcement Learning in Contextual MDPsOct 2024
Sample complexity is one of the core challenges in reinforcement learning (RL)[1]. An RL agent often needs orders of magnitude more data than supervised learning methods to achieve a reasonable performance. This clashes with problems with safety requirements, where the agent should minimize the …
KTKelvin ToonenMore info
Thiago Simão
-
(closed) Understanding deep learning: linear vs non-linear models and their dynamics of learningJul 2024
This project is finished/closed.While deep learning has become extremely important in industry and society, neural networks are often considered ‘black boxes’, i.e., it is often believed that it is impossible to understand how neural networks really work. However, there are a lot of aspects …
More info
Hannah Pinson
-
Evaluating Explanations of Model PredictionsMay 2024
The black-box nature of neural networks prohibits their application in impactful areas, such as health care or generally anything that would have consequences in the real world. In response to this, the field of Explainable AI (XAI) emerged. State-of-the-art methods in XAI define a …
ALAlexander LiuMore info
Sibylle Hess
-
Understanding Out-of-distribution DetectionMar 2024
There are numerous methods for out-of-distribution (OOD) detection and related problems in deep learning, see e.g. [1] for an overview. Many of these however only work well in highly fine-tuned settings and are not well understood in broader context. In this project, you would …
PTPim Tholhuijsen
Sibylle Hess
More info
Jan Moraal
-
Diversity of recommendations in collaboration with Bol.comJan 2024
— completed —Recommender Systems (RSs) have emerged as a way to help users find relevant information as online item catalogs increased in size. There is an increasing interest in systems that produce recommendations that are not only relevant, but also diverse [1]. In addition to …
Mykola Pechenizkiy
More info
Hilde Weerts
-
k-Nearest Neighbor Imputation Under Monotonicity ConstraintsNov 2023
See PDF
IVIko Vloothuis
Wouter Duivesteijn
More info
Rianne Schouten
-
(self-defined project on model-centric SD)Jul 2023
(irrelevant for self-defined project)
NSNiels SchellemanMore info
Wouter Duivesteijn
-
(self-proposed project on time-dependent SD)Jul 2023
(irrelevant)
BEBart EngelenMore info
Wouter Duivesteijn