Background: 

Large Language Models (LLMs) have recently demonstrated significant potential to improve access to healthcare data, offering new ways for patients to interact with their electronic health records (EHR). The adoption of Fast Healthcare Interoperability Resources (FHIR) as an open standard enables structured, interoperable health information exchange within and between healthcare settings. Integrating LLMs with FHIR applications could empower patients to ask questions and retrieve information from their EHRs using natural language. As highlighted in Ma et al. (2024; PMID: 40373519), LLMs can interpret and respond accurately to clinical queries formulated in plain language, bridging the gap between complex medical data and patients’ information needs. This technology has the potential to demystify health data, enhance transparency, and promote active patient engagement in care decisions. Despite promising results in experimental settings, the practical application of LLM-driven, FHIR-enabled chat systems for patient use remains largely unexplored. Questions remain about the reliability, clarity, safety, and real-world usability of such interfaces. This project will test an LLM on a FHIR application designed for patient interaction, evaluating its ability to support secure, accurate, and user-friendly access to personal health information. 

Aim: To develop and validate a Dutch-language, FHIR-integrated LLM application that empowers patients by providing accurate, user-friendly access to their health records and integrated clinical conversation data.

Project description: 

This project follows a multi-phased approach to bridge the gap between technical innovation and clinical practice, moving from initial development to real-world evaluation. The student will contribute to the following activities: 

Phase 1: Develop a Dutch-language mobile application based on the open- source Stanford model (https://pubmed.ncbi.nlm.nih.gov/40373519/) 

Phase 2: Validation by phycisian evaluators - evaluate the accuracy, safety, and clinical appropriateness of the LLM-generated responses. 

Phase 3: Implementation in the patient population - assess usability, comprehension, trust, and impact on patient engagement. 

Phase 4: Integration with Ambient Listening (which automatically captures and analyzes spoken clinical conversations in the background to convert them into structured, usable healthcare data) for patients.

Target Student: 

This project is particularly suitable for: 

1. Computer Science, AI, or (Bio)medical students with a strong interest in technical implementation. 

2. Students who speak Dutch fluently, as the application and patient interactions are in Dutch. 

3. Students interested in data standards (FHIR), Generative AI, and their practical applications in a clinical setting.

4. Individuals passionate about patient empowerment and transparency in healthcare.

Learning Opportunities and Added Value: 

1. Students will gain hands-on experience with LLMs and FHIR APIs – two of the most influential technologies currently shaping the future of digital health. 

2. Students will become part of a young, interdisciplinary, and growing research team at the Applied AI Accelerator (A3) Lab, consisting of medical doctors, PhD students, engineers and application specialists. This offers a unique environment tolearn how clinical care, technology, and research intersect in practice. 

3. Motivated students who show strong engagement and high-quality work may have the opportunity to contribute to scientific presentations and publications.    Supervision and Setting:  

Principal Investigator: Rosanne Schoonbeek, MD PhD 

The project will be supervised by the research team. The duration, workload, and academic output (e.g. research report, thesis, or presentation) can be tailored to the student’s educational program.

Application procedure: Interested students please share your CV and a motivation letter of max 200 words to i.jagota@umcg.nl, Director, A3 lab, UMCG

Background and Motivation 

Modern supervised deep learning mod Group-wise Argumentative Explanations and Natural-Language Summarization for Healthcare Models, especially when applied to healthcare tabular datasets, often behave as opaque “black boxes.” While their predictive performance can be strong, understanding how these models reason across different patient subgroups remains challenging and critical for clinical trust and adoption. 

The SpArX (Sparse Argumentative Explanations) framework offers a novel approach to explain neural networks by translating Multi-Layer Perceptrons (MLPs) into Quantitative Argumentation Frameworks (QAFs) via sparsification. By doing so, SpArX reveals how neurons and connections contribute to decisions in ways that go beyond traditional input-feature attributions, producing explanations that are mechanically faithful to the network’s operations. SpArX produces both global and local explanations and has been shown to outperform certain existing explainability techniques in faithfulness and insightfulness. However, healthcare datasets often exhibit complex data clusters - groups of samples that behave similarly under a model yet may represent distinct sub-populations. Identifying global explanations that summarize how a deep model reasons for each such group, and translating these explanations into human-understandable narratives, remains largely unexplored. 

This project extends SpArX to support group-wise explanation extraction and natural language explanation generation for neural network models trained on healthcare tabular datasets.

Aim 

The aim of this bachelor project is to develop and evaluate a framework that: 

1. Identifies groups of data points from a tabular healthcare dataset that share similar behaviour under a trained deep neural model. 

2. Extends SpArX to compute group-specific global explanations by analysing activations across the deep network layers for each group. 

3. Generates conversational natural-language explanations from these group- wise SpArX explanations using large language models (LLMs) to provide actionable insights to healthcare experts. 

4. Evaluates the generated explanations with domain experts to assess usefulness and interpretability.

Target Student

This project is particularly suitable for a student who: 

1. Has a solid foundation in Machine Learning and Deep Learning. 

2. Is interested in Explainable AI (XAI) and healthcare applications. 

3. Is comfortable with tools such as Python, PyTorch, clustering, and LLM APIs. 

4. Enjoys bridging technical frameworks with human communication.

Learning Opportunities: By completing this project, the student will: 

1. Gain hands-on experience with explainable AI frameworks and real healthcare data.   2. Learn to integrate multiple methods: deep learning, data clustering, argumentative explanations, and language models. 

3. Contribute to cutting-edge research at the intersection of AI interpretability and clinical decision support.  

  Other possible projects 

1. Implementing different functionalities to reduce administrative burden for clinicians (1)Discharge summaries, (2)Augmented Response Technology to generate drafted replies to patient questions, (3)Note summaries, (4)Ambient listening: Ambient listening for patients: Ditto à enabling patients to better understand their disease.

2. Computer Vision: AI-assisted laryngoscopy

Application procedure: Interested students please share your CV and a motivation letter of max 200 words to i.jagota@umcg.nl, Director, A3 lab, UMCG

Project description:

We are seeking a motivated student to develop advanced predictive models for CT imaging at the UMCG. This project focuses on training Physics-Informed Neural Networks (PINNs) to predict radiation dose and image quality parameters with superior accuracy compared to traditional mathematical models.

The Challenge

How can we determine CT scanner performance without extensive physical testing? By combining physics-based knowledge with machine learning, we aim to create a flexible model that adapts to any CT scanner with minimal calibration data.

Your Role

You will review existing open source mathematical models for CT prediction, use datasets from phantom experiments, and design a PINN architecture incorporating fundamental CT physics principles. You'll train and validate the model using phantom scan data, then compare performance against conventional approaches.

What We Offer

This project provides hands-on experience at the intersection of medical physics, deep learning, and clinical imaging. You'll work within a multidisciplinary team including a machine learning expert, an AI orientated radiologist, and a medical physicist. Depending on your performance, this may lead to thesis work, conference presentations, or co-authorship on scientific publications.

Who We're Looking For

Students with background in applied mathematics, data science, computer science, physics, or related fields. You should have programming skills (Python preferred), strong analytical abilities, and motivation for clinical research. Experience with neural networks is valuable but not required. Duration: 3-6 months (flexible for bachelor/master level)

Introduction:

Many scientific and technical images contain complex filamentary structures that are difficult to detect and represent automatically. The Locally Aligned Ant Technique (LAAT) is a recently developed structure-finding algorithm that was originally designed for high-dimensional point cloud datasets. It is based on efficient evolutionary computation and the idea of a swarm of 'ants' navigating the data and reinforcing coherent structures through local alignment rules and pheromone accumulation. This project aims to combine computer graphics concepts with a biologically inspired algorithm to detect and model structures directly from pixel maps with operations used in vector graphics.

Problem description:

Traditional image processing pipelines often struggle with noisy, curved or overlapping structures. The goal of this project is to design and evaluate an LAAT-based algorithm that can:

- detect elongated structures directly on pixel maps;

- robustly handle noise and local curvature;

- model the output as smooth vector curves suitable for analysis or visualisation.

The challenge lies in integrating these vector-graphics operations into the LAAT framework, enabling agents to detect, reinforce and model structures as vector-like geometric objects (curves, polylines and splines).

Relevance:

Vectorised structure extraction has a wide range of applications, including in astrophysics, medical imaging, materials science and computer graphics. A successful prototype would produce more accurate, editable representations of structures than classical pixel-based methods. There is already external scientific interest in a LAAT extension based on vector graphics, particularly from astrophysicists who work with filamentary structures in simulations and observational data.

For more information, please contact: Kerstin Bunte (k.bunte@rug.nl). Simone Vilardi (s.vilardi@rug.nl).

Introduction:

RH AgSystems was founded in 2020. A young company with 20 years of experience in soil sensors and precision agriculture. With their products they contribute to maintaining fertile soil and improving the soil where possible. After all, the soil is the basis for a large part of our food production and we need to treat it sustainably. The company consists of a small team of highly skilled and experienced employees. In addition to their knowledge of soil, they have specialist expertise in sensors, electronics, and agriculture to analyze the composition of the soil.

Their core technology is based on the measurement of gamma particles from soil with mobile sensor systems for precision agriculture. This passive gamma technology is a very powerful tool to determine soil texture and nutrients. The statistical modelling of soil properties based on the sensor values is currently a human process. In order to facilitate growth RH3S is exploring the possibilities of machine learning to further automate this modelling process.

Problem Definition:

The primary challenge addressed by this research is the identification and implementation of effective machine learning algorithms to detect soil composition based on gamma particle measurements, with a focus on efficient and interpretable techniques to automate the currently manual evaluation. The project is at the heart of many important questions for Europe, including sustainably, environmental protection, food production the successful integration of machine learning techniques for agricultural applications, with great potential of societal impact.

Methodology:

- the scope will depend on the ECTs (BSc or MSc thesis project) and flexibly defined with the interested candidate

For more information contact: Kerstin Bunte (k.bunte@rug.nl)

Introduction : Many practical problems require estimation of some parameters from a given set of observational data. In critical domains, such as biology and medicine, where data often is rare and corrupted with noise, people compute the so called posterior distribution to reflect the likelihood to which certain parameters explain the observed data, since the posterior also reflects the uncertainty. However, the computation of the posterior is usually not analytically tractable and grid approximations limited in the number of paramters that can be handled. Therefore, parameter estimation often relies on so called sampling methods, that draw points from high dimensional spaces. There is an added level of complexity in dynamic systems the so called Structurally Unidentifiability (SUI) which include many medical and biological examples, as it bases on the limitation of measurability, such that not all “states” of the dynamical system are accessible to observation. Hence, it is not possible to determine all the parameters of the system uniquely from the experimental data. Many popular Markov chain Monte Carlo techniques are known to perform inefficiently in these cases.

Problem description: Nested Sampling is expected to do better in the previously described scenarios. We have demonstrated this in two dimensional examples by comparing the density estimates produced by different samplers with the ground truth using appropriate distance measure. The goal of this project is to compare the performance of various samplers in higher dimensional problems. The crux is, that the quality evaluation of the estimate of these complex posterior distributions in higher dimensions is tricky. As mentioned before it is analytically not tractable to compute a ground truth to compare to. However, we have additional information about the posteriors, that we can analytically compute. In this project the aim is to utilize this knowledge extracted from the dynamical systems to compare the posterior distributions in areas where it matters.

Aimed output:

1. Identify relevant class of state-of-the-art samplers used with their optimal/ typical settings.

2. Identify and experiment with interesting higher dimensional problems to compare the performance of various samplers.

3. Identify optimal distance measures between distributions and also consider optimal criteria to compare performance of samplers.

The rise of antimicrobial resistance (AMR) is one of the most pressing global health challenges of our time, with projections indicating 10 million deaths annually by 2050. Rapid, accurate, and accessible infection diagnostics is crucial for treating infections, guiding targeted antimicrobial therapy, and preventing outbreaks. However, current diagnostic tools often fail to provide the speed, accuracy, and scalability needed. In addition, they require laboratory infrastructure and trained staff.

To bridge this critical gap, our project—DRAIGON—is developing an advanced diagnostic system based on accelerated whole genome sequencing combined with AI-assisted bioinformatics analysis. This innovative approach enables rapid pathogen detection, antimicrobial susceptibility profiling for virtually any drug-bug combination, and outbreak tracking in a single, streamlined assay. Our goal is to create a cutting-edge in vitro diagnostic solution that is not only highly precise but also easy to implement, even in resource-limited settings with high AMR burden.

As part of this multidisciplinary effort, we invite a highly motivated master student to contribute to the bioinformatics and AI-driven analysis that powers DRAIGON. Working with an international consortium of experts in sequencing technologies, clinical microbiology, machine learning, and infection control, the student will gain hands-on experience in:

• Developing and optimizing bioinformatics pipelines for pathogen identification, AMR gene detection, and outbreak clustering.

• Expanding AI-based antimicrobial susceptibility testing (gAST) models to ensure accurate classification of pathogens and their resistance or susceptibility in bloodstream and prosthetic joint infections (as a proof-of-concept).

• Integrating and refining a cloud-based interactive reporting system, enabling real-time diagnostics and decision-making in clinical settings.

This project offers a unique opportunity to work at the intersection of computational biology, artificial intelligence, and clinical microbiology, contributing to a real-world solution in the global challenge of AMR. Further aspects of the project are work-flow, regulatory, cost-effectiveness and societal impact. The work will directly impact the translation of genome-based diagnostics into routine clinical practice, helping shape the future of precision medicine and infection control.

If you are passionate about bioinformatics, AI-driven healthcare solutions, and global health impact, we encourage you to join us in advancing this next-generation diagnostic technology.

Introduction

Philips Drachten plays an important role in driving innovation in the Personal Health industry, having two of the largest Philips innovation sites globally. With over 2000 employees from over 35 different nationalities, Philips Drachten has some of the best consumer product developers in the world that work together to create innovative products with excellent end-user experience.

Problem description

The Process Capability Index (Cpk) is a parameter that measures how well a process performs in relation to its specification limits. In assembly and production lines, Cpk is used to measure performance, monitoring quality and ensuring consistency. A high Cpk value indicates that the process variation is small relative to the specification limits, suggesting a stable and capable production process. The main limitation of using Cpk on its own is that it assumes that the process is stable, and the data is normally distributed, which might not always be the case. Additionally, Cpk only provides a snapshot of the process capability and doesn't address the causes of variation. For a comprehensive quality assessment, it's important to use Cpk alongside other metrics and continuous monitoring.

Relevance

Combining Cpk with other statistical metrics and Machine Learning (ML) techniques can help production lines by providing a deeper, more detailed understanding of process performance and quality. While Cpk offers valuable insights into process capability, other metrics can address its limitations and provide a fuller picture. Also, integrating ML techniques enables predictive maintenance, anomaly detection, and real-time optimization. This combination would open the possibility to proactively manage production, reduce waste, and consistent high-quality outputs.

Aimed output

(1) Investigate the effect of alternative capability metrics in the measuring of performance for production lines;

(2) Identify important elements that influence production line capability and performance;

(3) Create ML algorithms to enhance capability monitoring;

(4) Provide advice regarding the benefits of combining multiple process performance metrics.

For more information, please contact:

André Stefan

andre.stefan@philips.com

Introduction

Philips Drachten is important for the future of Personal Health, driving key innovations. At the Drachten site, we develop and produce high quality innovative consumer-oriented products that improve people’s lives around the world. Examples are shavers, beard trimmers, epilators and electric toothbrushes. Philips Drachten has been the development and production center of the advanced electronic Philips Shavers since 1950, having one of the biggest development and production centers in Europe. Philips Drachten has 2,000 employees, including 600 developers drawn from among 35 nationalities.

Problem description

Many of the Philips Personal Health products have an electromechanical drivetrain which generate and transfer motion to a specific load. The design of such drivetrains is often driven by mathematical models that describe their motion dynamics. Once the design has reached a certain maturity, prototypes are build and tested. Part of this testing is to determine how the prototype compares to the original design. System identification techniques are applied to compare the dynamic behavior of the prototype with the used model. However, it is also of interest what the physical parameter values are that together with the model describe the behavior of the prototype.

Relevance

The motion made by the drivetrain in the Philips products is important for functional performance; examples are the motion made by the blade of a trimmer or the motion of the brush-head of an electric toothbrush. Being able to identify the dynamic behavior of a drivetrain sample, and its corresponding physical parameters, gives a lot of valuable information that can be used for further optimization of the design and/or the production process. Furthermore, knowledge of these parameters allow for the development of new smart functionalities.

Aimed output

(1) For a selected product drivetrain, use simulation data to train one (or more) ML algorithm to identify the physical parameters of the drivetrain.

(2) Test the selected ML algorithm with measurements from real drivetrains.

(3) Develop an easy to follow protocol such that others can also do the measurements and estimation.

(4) Minimize the required measurement and processing time.

For more information, please contact:

Daniel Dirksz

daniel.dirksz@philips.com

0031(0)624151619

Background

Kidney transplantation is recognized as the only curative treatment for end-stage kidney disease (ESKD). Annually, around 100,000 kidney transplantations are performed worldwide, and roughly 1,000 kidney transplants in the Netherlands. Half of these transplants are from living kidney donors (LKD) as opposed to deceased donors, where one of the two kidneys is donated. While most of the research in kidney transplantation focuses on transplant recipients, much fewer studies assess the impact transplantation has on living kidney donors. Although there is evidence showing living with one kidney after donation is generally safe and has minimal impact on health and quality of life, there is a slight but measurable long- term increase in the risk of ESKD and hypertension, as well as other potential complications.

In LKD after transplantation, the estimated glomerular filtration rate (eGFR, a surrogate for kidney function) is reduced by 35% on average. Although total kidney tissue abruptly decreases by 50%, a disproportionately smaller reduction is observed in kidney function. This finding indicates a compensatory functional increase of the remaining kidney under increased demand. The adaptive ability of the kidney that allows for retaining more than 50% of kidney function is referred to as the renal functional reserve (RFR). Previous studies have shown that this ability varies significantly among individuals and can be partially explained by, among others, donor age, demographics, body mass index (BMI), and baseline kidney function.

Previous studies have investigated clinical features associated with RFR; however, the contribution of molecular features, such as gene expression, to inter-individual RFR variability remains insufficiently explored. The information contained within this modality holds the potential to enhance pre-donation risk stratification and shed light on the physiological mechanisms driving kidney adaptation. The study’s objective is to explore transcriptomic data in search of patterns associated with RFR variability.

Dataset

This study consists of data from 31 living kidney donors. For each donor, gene expression measurements (approximately 60,000 features) were obtained before kidney donation through next-generation RNA sequencing. eGFR measurements were recorded both pre-donation and at multiple intervals post-donation (3 months, with planned follow-ups at 5 and 10 years). This dataset provides a unique opportunity to explore the relationship between high-dimensional transcriptomic profiles and RFR.

Additionally, the project is expected to expand with the addition of metabolomics and lipidomics data from the same cohort in the near future. This means that the project has the potential to evolve into a challenging multi-omics study, where students will have the opportunity to acquire valuable skills in integrating and aligning diverse data modalities to investigate patterns across multiple information sources in relation to kidney function outcomes.

Expectations

If you’re interested in working with high-dimensional biomedical data, starting with transcriptomic analysis and potentially expanding to more complex multi-omics data, this project might be for you! It offers opportunities to learn conventional biomedical data analysis pipelines, integrate and align multi-modal datasets, apply techniques for handling small sample sizes (e.g., transfer learning frameworks), and develop a unique skillset in cutting-edge biomedical data science.

Our vision is to make clean energy accessible to everyone. By contributing to a just and future-proof system. We will provide smart solutions to give insights for optimal customer engagement, load shisfting, and demand forecasting. Load disaggregation is essentially breaking down the total consumption of the household to the consumption of each appliance, by using algorithms and AI.

There are several directions that can be discussed and specified with interested candidates to fit it with their background:

(1) Anomaly detection: Investigate ways to detect irregular patterns in the consumption of the appliances. These irregularities could be due to inefficient use from theconsumer, malfunction or degradation of the appliance, etc. The input of theanomaly detection system should be the total consumption of the house (possible load disaggregation as a middle step)

(2) Explore non-DL solutions for load-disaggregation: Last years the academia has shifted from traditional machine learning approaches (mainly Hidden Markov Models) to mainly DL Neural Networks so it would be interesting to explore other approaches. Maybe a statistical/traditional ML approach which is faster and requires less data would be beneficial for us.

(3) Work on the existing models: Optimize hyper-parameters, tackle challenges with specific appliances. Research on the effect of adding hand-crafted or secondary features to the input signals of DL models (e.g. time, number of occupants, number of bedrooms etc)Use test's house total consumption for pre-training (e.g. electricity). Identify appliances activations with confidence

(4) Solar generation problem: Estimate the solar production of houses, given the weather conditions (we assume no information regarding the solar panel system, and no information on the ground truth for a test house)

(5) Load forecasting on household level: Forecast the electricity demand for a house, incorporating/assuming the results of load disaggregation (assume you know information for the consumption of some appliances)

(6)Estimate house parameters given the total consumption: Parameters like the size of the house, number of occupants, construction year, energy label. Device detection: Identify which devices exist in a house (not how much they consume), only by looking at the total consumption (with confidence level as well).

Please contact: k.bunte@rug.nl and marios@powerchainger.nl for more information

About Powerchainger

We are a climate tech startup company located at Zernike Campus Groningen. At Powerchainger we are passionate about energy and technology. With a small and highly motivated team, we develop cutting-edge AI software for using energy in an optimal way and minimizing energy waste. Currently, we are at the stage of Product Soft Launch, developing an MVP for market. With our product, we are ready to build a smarter, fairer, and more sustainable energy system.

We are offering student projects in collaboration with RUG (either as an internship project, or a thesis/graduation project), and we are looking for motivated students to join us.

Projects:

1) Load forecasting on household level: Forecast the electricity consumption for a house (or a group of houses), incorporating the results of load disaggregation (assume you know information for the consumption of some appliances). This is an important issue for the energy providers, and there is a lot of room for improvement and impact.

We aim to demonstrate that by incorporating load disaggregation insights, we can improve the accuracy of demand forecasting algorithms.

2) Improve Deep Learning (DL) load disaggregation models: This project is aimed at exploring improvements in DL models, in order to tackle challenges with specific appliances.

Some possible directions are:

- Add hand-crafted or secondary features to the input signals of DL models (e.g. time of day, number of occupants, number of bedrooms etc.)

- Use a test's house total consumption for pre-training.

- Identify appliances’ activations (ON/OFF) with a confidence score. Load disaggregation is giving detailed insights to households, which can be used to steer people into using energy during more optimal times (load shifting).

3) Explore non-DL solutions for load-disaggregation: It has been noted that DL neural networks sometimes struggle to perform well for specific appliances.

In this project we aim to explore some statistical/traditional ML approaches that are more accurate for specific use cases, and/or require less data.

Contact: Kerstin Bunte (k.bunte@rug.nl) and Marios Souroulla (marios@powerchainger.nl)

Introduction

Plastic pollution has emerged as one of the most pressing environmental challenges of our time. Despite the widespread use of plastics in modern society, current waste management systems are inadequate to handle the massive scale of plastic production, leading to persistent environmental contamination that threatens ecosystems worldwide. Traditional recycling methods face significant limitations, particularly in creating closed-loop systems where recycled materials can match the quality of virgin plastics.

Biological degradation of plastics represents a promising solution to this global crisis. Unlike conventional recycling approaches, biodegradation offers the potential for complete breakdown of plastic polymers into their constituent monomers, enabling true circular economy approaches. However, this field remains significantly under-researched, with limited understanding of naturally occurring microbial communities capable of plastic degradation.

Recent studies have demonstrated that certain microorganisms possess enzymes capable of breaking down specific plastic polymers. Environmental bacteria, having evolved in increasingly plastic-contaminated environments, may have developed novel enzymatic pathways for plastic utilization. This natural adaptation presents an untapped resource for discovering new biodegradation mechanisms that could be scaled for industrial applications.

Problem Definition

The primary challenge addressed by this research is the identification and characterization of plastic-degrading bacteria from environmental samples, with the ultimate goal of discovering novel enzymes responsible for plastic biodegradation. Specifically, this project aims to:

Primary Objective: Isolate and characterize bacterial communities from environmental samples that demonstrate the ability to degrade or modify different types of plastic polymers, with a focus on polyethylene (PE), polypropylene (PP), and polyvinyl chloride (PVC).

Key Research Questions:

- Which bacterial species from environmental samples can adapt to grow on plastic substrates as primary carbon sources?

- What enzymatic pathways are responsible for plastic degradation in these bacterial communities?

- How does bacterial community composition and gene expression change in response to different plastic substrates?

- What novel enzymes can be identified through metagenomic and metatranscriptomic analysis of plastic-enriched bacterial cultures?

Methodological Approach:

The project will employ a comprehensive multi-omics approach combining environmental microbiology with advanced molecular techniques. Environmental bacterial samples will be collected and subjected to plastic enrichment protocols using different polymer substrates. Pre- and post-enrichment samples will be analyzed using:

- Metagenome sequencing to identify bacterial community composition and potential plastic-degrading genes

- Metatranscriptome analysis to determine active metabolic pathways and gene expression changes during plastic exposure

- Enzyme activity assays to quantify degradation capabilities

- Comparative analysis across different plastic types to identify substrate-specific adaptations

This research addresses a critical knowledge gap in understanding natural plastic biodegradation mechanisms while providing a foundation for developing biotechnological solutions to plastic pollution. The discovery of novel plastic-degrading enzymes could enable the development of more efficient biological recycling processes, contributing to sustainable waste management strategies.

For more information contact:

the CS supervisor: Kerstin Bunte (k.bunte@rug.nl) and Chemistry supervisor: Edita Jurak (e.jurak@rug.nl)

Project Description

Sparse neural networks can match dense models with far fewer parameters, yet the mechanisms behind this efficiency are not well understood. This project studies the self-organizing principles of Truly Sparse Ensembles (TSE) [1], where networks are sparse from initialization and adapt their connectivity during training. The focus is on explaining why certain sparsity regimes work, rather than tuning hyperparameters.

Research Questions

What mechanisms enable efficient learning in sparse networks? How do network structure and training dynamics vary across sparsity levels and tasks?

Approach

The student will train ensembles of shallow MLPs using TSE on regression tasks across varying sparsity levels, and develop analysis tools to study the resulting networks:

Connectivity patterns: What structures emerge during training, and how do they relate to performance?

Weight dynamics: How do weight distributions evolve? Are there characteristic signatures of successful learning?

Functional properties: How does sparsity shape the learned input–output mapping and implicit regularization?

If clear principles emerge, the student may propose a simple adaptive sparsity mechanism informed by the empirical findings.

Expected Outcomes

A mechanistic understanding of sparse network learning, identifying the structural and dynamical properties that underpin strong performance. Output includes a thesis. Results could contribute to scientific publications

Synergy with Theory

The project complements a parallel statistical physics effort, with a bidirectional exchange: empirical findings inform theory, and theoretical predictions are tested through targeted experiments, forming a tight feedback loop.

Contact person: Otavio Citton (o.c.citton@rug.nl)

References

[1] van der Wal, P. R., Strisciuglio, N., Azzopardi, G., & Mocanu, D. C. (2025). Multilayer perceptron ensembles in a truly sparse training context. Neural Computing and Applications, 1-20.

Project Description

Sparsity is an important feature to ensure energy efficiency and faster training in neural networks, however, on a theoretical level, it is not that well understood. This project aims at a theoretical analysis, using techniques from statistical physics [1], to study the role that sparse connectivity of the weights in shallow neural networks has on the behavior of the network such as phase transitions, asymptotics and training dynamics.

Research Questions

How does sparsity, and the topology of the weights in general, affect phase transitions in an ensemble of shallow neural networks? How does this behavior change with quenched and annealed dilution?

Approach

To model sparsity in this framework we use dilution, where we attach a Bernoulli random variable to each network weight [2]. Each configuration of these variables corresponds to a particular pruning of the network. The goal will be to study how the dynamics of these dilution variables change the behaviour of the network. For the equilibrium analysis, the student will perform the partition function calculation using techniques such as saddle point method and replica method [3]. For the dynamics analysis we will focus on the online stochastic gradient descent algorithm, and obtain differential equations for the order parameters that describe the average behavior of the network [4].

Expected Outcomes

Learn how to use statistical physics techniques to analyze complex systems. Obtain the learning curves for the system annealed/quenched dilution. Understand the role that the sparsity has on these systems. The project result would be considered satisfactory if either the equilibrium or the dynamics analysis is performed. Output includes a thesis and the results could contribute to scientific publications in specialized journals.

Synergy with Experiments

The project is part of a collaboration with another, more applied, machine learning project that aims at understanding learning dynamics in truly sparse ensembles. These two projects should complement each other and both students can expect co-supervision and discussions between them.

Remarks

Part of the work for the equilibrium analysis has already been done, so we expect that the student can take from where we stopped. The project was designed for a master’s student, but can be adapted for a bachelor’s project.

The project description comprises both the equilibrium and dynamics analysis, however, these can be split into two projects. In this case we can discuss what would be considered sufficient depending if it is a bachelor or master thesis.

Contact person: Otavio Citton (o.c.citton@rug.nl)

References

[1] Engel A.; Van den Broeck C. Statistical Mechanics of Learning. Cambridge University Press; 2001. [2] Barbato, D. M. L.; Fontanari, J. F. . (1995). Dilution in a linear neural network. Physical Review E, 51(6), 6219–6229. doi:10.1103/PhysRevE.51.6219 [3] Citton, Otavio; Richert, Frederieke; Biehl, Michael . (2025). Phase transition analysis for shallow neural networks with arbitrary activation functions. Physica A: Statistical Mechanics and its Applications, 660, 130356. doi.org/10.1016/j.physa.2025.130356 [4] Saad, David; Solla, Sara A. . (1995). On-line learning in soft committee machines. Physical Review E, 52(4), 4225–4243. doi:10.1103/PhysRevE.52.4225

MTO is an astronomical source-finding tool based on max-trees and the use of chi-squared statistics. It has shown great performance for images in which the number of photons detected per pixel is high enough to allow the Poisson noise in the signal to be approximated by a Gaussian distribution with variance scaling linearly with the mean. In certain imaging modalities, such as in STED microscopy, photon counts are far too low to make this work reliably. There are several better statistical tests that could be used in this situation, but such an adaptation has yet to be realised.

The aim of this project is to explore suitable statistical tests, and test this on real and simulated STED microscopy data.

Number of positions: 5

Within the DigiAgro3 project, we are looking at methods for detection of plant disease for crop monitoring. A database of images and labels in the form of bounding boxes is available to train and test ML techniques for this task. The aim of this project is to implement different types of feature extraction methods, including texture, colour, and morphological profiles, and optimizing dissimilarity measures and machine learning approaches to differentiate healthy from diseased leaves, using as little training data as possible. In this project, up to five students can participate, each choosing a particular set of methods to implement and test. At the end, a full-blown comparison will be performed.

Num of positions: 2

Within the DigitAgro3 project a data set has been created using a drone flying over a field. The aim is to detect plant stress in the field using multi-spectral data collected with the camera on the drone. In this project you will explore a number of different image analysis methods to find diseased plants, or plants that lack nutrients. Methods to be explored include so-called alpha trees and multi-spectral texture methods. Ideally, the system would require very little in the way of annotated data.

This is a project in conjunction with a company, and will explore multiple methods (morphological, texture segmentation, deep learning) allowing multiple students to work in a larger team. More details to follow.

In astrophotography, it is often hard to perform contrast stretching of faint, diffuse nebulae without blowing out the stars. Astrophotographers often separate the stars out, stretch the nebula layer, and then add the stars back in. This often uses deep learning methods such as StarNet++. This is rather resource hungry. The idea of this project is to use the max-tree of the image to split the sources into compact sources (stars) and extended, diffuse ones, and only stretch the latter, without explicit separation of layers. The aim is to make a demo program with GUI to allow interactive setting of parameters.

Num of positions: 3

A hyper-spectral image data set is being gathered, aimed at determining different plant stress factors, such as disease, lack of nutrients, drought, salinity, etc. Unlike multi-spectral images, which have between 4 and a few tens of spectral bands, hyperspectral images can have many hundreds of spectral channels, each less than one nanometer wide. One could say each pixel contains a function, rather than a vector of a few features. The aim of this project is to explore methods of identifying stress factors using this fine-grained spectral information. Furthermore, we want to develop optimal dissimilarity measures to segment such images using alpha trees. Finally, we want to simulate multi-spectral data, by binning the hyperspectral data. This way, we could investigate what minimal set of spectral bands would be optimal for this kind of stress detection.

Number of positions: 5

We have a few vacancies for projects on building high-performance and trustworthy autonomy in mobile robotics. The topic lies within but not restricted to the following domains:

(1) Dynamic state estimation using multimodal sensors such as inertial measurement units, light detection and ranging (LiDAR) sensors, RGB(-D) cameras, ultra-wideband (UWB) sensors, dynamic vision sensors, etc.

(2) Mobile scene mapping and understanding on smart edges

(3) Motion planning and locomotion of quadrupedal robots

(4) Collaborative multi-robot exploration

Our projects typically involve both algorithmic innovations and real-world validations. It is also possible that a project is theoretic-oriented. Each project will be tailored according to the student’s interests and skills. Students are also encouraged to come with their own ideas, and we are open to provide supervision. See more information here: https://asig-x.github.io/

Required:

(1) Solid programming skills in C++, Matlab or Python, pre-knowledge in ROS(2) is a plus

(2) Good at and comfortable with mathematics, happy to work on hardwares and “get hands dirty”

(3) Strong motivation and dedication in developing cutting-edge technologies, excellent communication skills, willing to work in teams

Contact me: Kailai Li (kailai.li@rug.nl)

Status: Open

No. of positions: 1-2

We are seeking a motivated student to develop machine learning algorithms for identifying and managing medical lines and tubes in radiological images at the UMCG. This project focuses on training deep learning models using publicly available datasets to either (1) detect lines/tubes and assess their positioning, or (2) remove lines/tubes and intelligently fill in the gaps.

The Challenge

How can we automatically identify medical lines and tubes in radiological images and determine if they are correctly positioned? Alternatively, how can we remove these devices to improve visualization of underlying anatomy? By leveraging computer vision and deep learning, we aim to create a robust application that works across different imaging modalities.

Your Role

You will review existing AI architectures for medical image analysis (U-Net, YOLO, or transformer-based models), utilize publicly available radiological datasets, and design an AI pipeline for either:

Option A: Detection and position assessment - identifying medical devices (endotracheal tubes, central lines, nasogastric tubes, etc.) and classifying their positioning.

Option B: Device removal and inpainting - segmenting and removing devices while using generative AI to realistically fill removed regions.

You will train and validate your model, implement evaluation metrics, and compare against existing methods.

What We Offer

This project provides hands-on experience combining computer vision, deep learning, and clinical radiology. You will work with a multidisciplinary team including a machine learning expert, an AI-oriented radiologist, and medical imaging specialists.

Depending on performance, this may lead to thesis work, conference presentations, or co-authorship on publications.

Who We are Looking For

Students with background in computer science, data science, AI, biomedical engineering, or related fields. You should have:

Strong Python programming skills (PyTorch experience preferred)

Knowledge of deep learning and computer vision

Experience with CNNs, object detection, or image segmentation is valuable

Motivation for clinical research

Status: Open

No. of positions: 1-3

Head and Neck Squamous Cell Carcinoma (HNSCC) is a common cancer in the upper airway and digestive tract, where timely detection and effective surgical treatment remain challenging in clinical practice. One major challenge is that doctors often need to manually review endoscopy videos and surgical procedures, which can be time-consuming and may vary between clinicians. To improve this process, our research aims to develop AI-based computer vision methods for understanding medical images and videos in head and neck oncology. We focus on three main directions:

(1) Real-time lesion detection in laryngoscopy videos to support automated screening and diagnosis. (2) Multimodal large language model (MLLM)-based detection and reasoning by combining endoscopy images with clinical text information. (3) Surgical head and neck video analysis for objective skill assessment, supporting surgeon training and enhancing surgical performance.

Status: Open

No. of positions: 2-3

Invasive aquatic plants disrupt biodiversity, clog hydraulic infrastructure, reduce navigability, and increase flood risk. Traditional detection methods are labor-intensive and often imprecise, especially under turbid water, sun-glint, and mixed vegetation canopies. Harkboot’s established “hark-method” (root removal) offers highly effective ecological control, but optimal deployment requires earlier, more precise detection. Then, this project will translate cutting-edge sensing research into a field-deployable engineering prototype, incorporating combinations of hyperspectral imaging, passive polarimetric sensing, and AI-based classification, integrated into UAV-based field workflows. This work directly supports municipalities, water boards, and ecological agencies tasked with meeting EU biodiversity and invasive species regulations.

In this project, you will be developing a machine learning algorithms for detecting and mapping aquatic vegetation using multimodal sensor data collected from UAVs and surface platforms. The project is in collaboration with Harkboot.nl and ENTEG.

Status: Open

No. of positions: 1-2

The u-serrated immunodeposition pattern of immunoglobulin G (IgG) in skin in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of epidermolysis bullosa acquisita (EBA), a severe autoimmune disease with skin blistering. Making the right diagnosis is challenging for clinicians and of importance for treatment and prognosis of this chronic disease.

The u-serrated pattern can be identified using immunofluorescence microscopy and based on medical images. The u-serrated pattern is explained by the localisation of IgG in skin, with the autoimmune response targeting type VII collagen. A previous study investigated the feasibility of CNNs for the recognition of u-serrated patterns in medical images, which showed an effective approach with a high accuracy (Shi et al. 2019, 2021).

In the project, we aim to validate CNNs in u-serrated pattern recognition in a large dataset of medical microscopy images from a prospective European multicenter study (MAXISPA), and explore potential new strategies to optimize pattern recognition in medical images.

Co-investigators:

Joost Meijer MD PhD, dermatologist at UMCG Center of Expertise for Blistering Diseases

Zixian Liang PhD candidate at UMCG Center of Expertise for Blistering Diseases www.immunoderma.org

References:

Shi, C., Meijer, J. M., Guo, J., Azzopardi, G., Diercksr, G. F. H., Schmidt, E., Zillikens, D., Jonkman, M. F., & Petkov, N. (2019). Detection of u-serrated patterns in direct immunofluorescence images of autoimmune bullous diseases by inhibition-augmented COSFIRE filters. International journal of medical informatics, 122, 27–36. https://doi.org/10.1016/j.ijmedinf.2018.11.007

Shi, C., Meijer, J. M., Azzopardi, G., Diercks, G. F. H., Guo, J., & Petkov, N. (2021). Use of Convolutional Neural Networks for the Detection of u-Serrated Patterns in Direct Immunofluorescence Images to Facilitate the Diagnosis of Epidermolysis Bullosa Acquisita. The American journal of pathology, 191(9), 1520–1525. https://doi.org/10.1016/j.ajpath.2021.05.024

Status: Open

No. of positions: 1-2

The common practice for machine learning applications is to feed as much data as the model can take. This is because in most machine learning applications feeding more data enables the model to predict better. However, few-shot learning aims to build accurate machine learning models with less training data. The metric-learning based few-shot image classification focuses on learning a transferable feature embedding network by estimating the similarities between query images and support classes from very few images.

In this project, we aim at improving the current few-shot learning approaches for image or video classification. TWO projects can be offered under image classification, one focuses on channel attention and the other on incorporating frequency information. One project is offered under video classification with a focus on vision transformer. For more information, please contact: j.guo@rug.nl.

References:

[1] Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo. Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning. https://arxiv.org/abs/1903.12290

[2]Hiller et al. (2022) Rethinking Generalization in Few-Shot Classification. NeurIPS.

[3] Cheng et al. (2023). Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

[4] Ren et al. (2026). Adaptive feature selection-based feature reconstruction network for few-shot learning. Pattern Recognition.

No of positions: 2-3

Project Description

Colorectal cancer is one of the most common cancers worldwide, and colonoscopy imaging plays a crucial role in early diagnosis and later surgical treatment. Modern machine learning methods for automated analysis of colonoscopy images (e.g., detection and segmentation of polyps or cancerous lesions) require large amounts of annotated data. However, medical image data are often limited due to privacy, ethical constraints, and the high cost of expert annotation.

The goal of this project is to generate realistic synthetic colonoscopy images and/or video frames using deep generative models, such as Generative Adversarial Networks (GANs) or diffusion models. These synthetic data can potentially be used for data augmentation and algorithm development. Several publicly available endoscopy datasets (e.g., Kvasir, Kvasir-SEG, or Hyper-Kvasir) will be used to explore methods to learn the visual characteristics of colonoscopy imagery, including textures, lighting conditions, and lesion appearance.

Possible Tasks

Depending on the interests, the project may include:

• Exploring and preprocessing public colonoscopy image or video datasets
• Implementing and training a generative model (GAN or diffusion-based) for image synthesis
• Conditional generation using auxiliary information (e.g., segmentation masks or lesion categories)
• Extending image generation to video frame synthesis with temporal consistency
• Evaluating the realism and diversity of synthetic images using quantitative metrics and visual inspection
• Investigating the usefulness of synthetic data for downstream tasks such as segmentation or classification

Prerequisites

• Basic knowledge of machine learning and deep learning
• Familiarity with PyTorch
• Interest in medical imaging and computer vision

In partially observed dynamic systems one can encounter situations where system identification is impossible. For the systems of interest this can lead to manifolds of parameters that lead to indistinguishable outputs.

In this project we would like to build a tool that allows us to investigate the indistinguishable solutions and present them to the user.

For this we will investigate the use of Generative topographic mapping (GTM) or Self-Organizing MAPs (SOMs) to build a 2-dimensional map that can be navigated to retrieve and represent the different solutions, to be investigated by the domain expert.

The candidate optimally has some prior knowledge in machine learning and is not scared of mathematics.

Some prior knowledge in ODE systems is helpful, but not strictly necessary. The interested candidates can contact Kerstin Bunte (k.bunte@rug.nl)

FDG-PET scans [a1,a2] provide 3D brain images of ca. 200000 voxels. This technique is used to diagnose a variety of neurodegenerative disorders such as Parkinson’s or Alzheimer’s disease [a1]. The use of machine learning with the aim of an automated diagnosis support system is challenging. Given the relatively small number of available data (scans of patients or healthy subjects) it is required to perform a dimensional reduction, i.e. a mapping of the scans to a low-dimensional feature space.

A1) Application of autoencoders for dimensionality reduction [Taken] As described in, e.g., [a1,a2] this is frequently done in terms of Principle Component Analysis based on a reference group. This SSM-PCA approach has become a standard technique in neuroimaging for the low-dim representation of FDG-PET scans.

In this project we want to overcome the limitations of the linear PCA based approach by using GMLVQ classifiers in combination with Auto-Encoder [a3,a4] networks. The latter are trained to identify low-dim. representations of FDG-PET scans in their bottleneck layer, which can be reproduced by the Decoder part of the network, see [a3] for the basic idea.

Supervision: Michael Biehl, Roland Veen, Sofie Lövdal

A2) Representation by regions of Interest (ROI) As an alternative low-dimensional representations of scans is in terms of pre-defined regions of interest (ROI) according to a given atlas. In this project we will compare the performance of ROI based classifiers with that of SSM/PCA. Possibly the comparison can be extended to the setup studied in (A1).

Supervision: Michael Biehl, Sofie Lövdal

References(A)

[a1] R. van Veen, S.K. Meles, R.J. Renken, F.E. Reesink, W.H. Oertel, A. Janzenl, G.-J. de Vries, K.L. Leenders, M. Biehl FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder Computer Meth. and Programs in Biomedicine 225: 107042 (2022)

[a2] R. van Veen, N. R. Bari Tamboli, S Lövdal, S.K. Meles, R.J. Renken, G.-J. de Vries, D. Arnaldi, S. Morbelli, P. Clavero, J.A. Obeso, M.C. Rodriguez Oroz, K.L. Leenders, T. Villmann, M. Biehl Supspace corrected relevance learning with application in neuroimaging Artificial Intelligence in Medicine 149: Art. No. 102786 (2024)

[a3] R.J. Veen, C. Hadjichristodoulou, M.biehl Interpreting Hybrid AI through Autodecoded Latent Space Entities ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024, i6doc.com publ., ISBN 978-2-87587-090-2.

[a4] C. Hadjichristodoulou (MSc Thesis, 2024) Dimensionality Reduction and Classification in High-Dimensional Data: A Hybrid Approach Using Generalized Matrix LVQ and Deep Learning Techniques https://fse.studenttheses.ub.rug.nl/34168/

These projects concern modifications and extensions of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ), a prototype-based classifier that is equipped with an adaptive distance measure for the comparison of data points and prototypes. It constitutes a transparent and interpretable framework for classification and facilitates the analysis of feature relevances, see [b1,b2].

Our recently suggested IRMA method is based on iteratively training a set of GMLVQ [1,2] systems. In each iteration, the adaptation of the relevance matrix is restricted to the subspace orthogonal the previously identified most relevant directions in feature space.

This provides more complete insight into the relevance of features and allows to construct robust improved distance-based classifiers [b3].

B1) Iterated Relevance Matrix Analysis (IRMA) combined with Boosting [taken]

Our recently suggested IRMA method is based on iteratively training a set of GMLVQ [b1,b2] systems. In each iteration, the adaptation of the relevance matrix is restricted to the subspace orthogonal the previously identified most relevant directions in feature space. This provides more complete insight into the relevance of features and allows to construct robust improved distance-based classifiers [b3].

In this project, we want to explore the possibility of combining the scheme with boosting by putting emphasis on those example data that have been misclassified in previous IRMA iterations.

Supervision: Michael Biehl, Roland Veen, Elina van den Brandhof, Sofie Lövdal

B2) Improved classification by GMLVQ ensembles

In this project we will consider the training of several GMLVQ systems [b1,b2] from randomized subsets of the available training data and/or randomized subsets of features. Similar to the concept of Random Forest (RF) classifiers [b4], we will combine the trained systems in an ensemble or committee. The ensemble will be investigated with respect to its classification performance and we will study the possibility to study feature relevances in analogy to the RF importances. If successful, time permitting or as a follow-up, using the new algorithm as a replacement for RF in the BORUTA method for relevance evaluation [b5].

Supervision: Michael Biehl, Roland Veen

Note that projects (B1) and (B2) are related and could be performed in close collaboration between two students.

B3) Extending the MATLAB GMLVQ toolbox (programming project)

Under https://www.cs.rug.nl/~biehl/mcode.html we provide a MATLAB toolbox for GMLVQ which is currently being refactored and extended. In this project, the above mentioned IRMA [b3] as well as other extensions, e.g. Angular LVQ, will be included into the next editions of the toolbox. A BORUTA toolbox for MATLAB is currently under development and can also be considered [b5] in parallel.

Supervision: Michael Biehl, Roland Veen

B4) Evaluating bias in the ADNI database by application of Iterated Relevance Matrix Analysis (IRMA) Iterated Relevance Matrix Analysis (IRMA) has recently been proposed as a method to identify class-discriminative subspaces, see Ref.[b3]. Extracting class-specific information out of a data set in this way set has potential as a means for bias removal. Neuroimaging and specifically brain PET scans have been shown to contain scanner/center/protocol-specific effects [a2]. We would like to apply IRMA on the open source data base ADNI, focusing on brain FDG PET images in Alzheimer’s disease. This project will evaluate the amount of scanner bias present in the separate resulting image data sets, as well as how the bias present may influence a machine learning model trained on this data.

Supervision: Michael Biehl and Sofie Lövdal

References (B)

[b1] P. Schneider, M. Biehl, B. Hammer, Adaptive Relevance Matrices in Learning Vector Quantization Neural Computation 21: 3532-3561 (2009)

[b2] M. Biehl, B. Hammer, T. Villmann, Prototype-based models in machine learning Advanced Review in WIRES Cognitive Science, 7(2):92-111, 2016

[b3] S. Lövdal and M. Biehl, Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces Neurocomputing 577: Art. No. 127367 (2024)

[b4] Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324

[b5] M.B. Kursa, W.R. Rudnicki Feature Selection with the Boruta Package Journal of Statistical Software, Vol. 36 (2010)

The Next Move in Movement Disorders (NEMO); project NEMO - Scientific research focuses on computer aided diagnosis of hyperkinetic movement disorders, which are characterized by an excess of involuntary movements, including tremor, myoclonus, dystonia, tics, chorea, spasticity and ataxia. Each movement disorder has its own clinical presentation, but frequently complex and variable mixed forms occur. Research has demonstrated that it is difficult for neurologists to distinguish these disorders if they do not see these patients frequently, and also that doctors do not always agree amongst themselves. Since hyperkinetic movement disorders have no clear anatomical abnormalities, pathology is most likely attributed to altered function of brain networks. However, so far, imaging studies have shown inconsistent distinctions in the topography of regional cerebral metabolism. This is likely due to the use of different methodologies in different groups of patients and inconsistent phenotyping. The NEMO project aims to improve patient diagnosis using computer aided methodology. In this project many different data acquisition includes multiple modalities such as video analysis, movement registration (accelerometry and EMG) and neuroimaging including functional magnetic resonance imaging (fMRI). Data acquisition for the NEMO project is ongoing. Currently, we have, for instance, EMG data from over 170 participants.

C1) Automatic detection of myoclonus bursts

As a follow-up of previous work, the goal of this student project is to automatically detect myoclonus bursts using machine learning. During the clinical diagnostic process, these bursts are often labeled by hand if clinical neurophysiology measurements are performed. This is done to quantify burst frequency and duration, which facilitates the subclassification of myoclonus and its neural origin (cortical or subcortical).

Supervision: Elina van den Brandhof and Michael Biehl (CS)

Level: Research Internship project or MSc thesis                     Research questions/tasks:

The project builds upon previous proof of concept studies, see e.g. https://fse.studenttheses.ub.rug.nl/31230 which addressed the automatic identification of myoclonic bursts in EMG data.

In this follow-up project we will consider the following questions: (1) Can the detection algorithm be improved with respect to implementation, performance and robustness? (2) The improved method and implementation will be tested on an available, extended data set (myoclonus bursts labeled data in NEMO and KNF data).

If time allows the following, additional research questions  and tasks will be addressed as well: (3) Review: Which supervised or unsupervised machine learning algorithms could be useful to cluster or classify  myoclonus bursts?     (4) Application: Can myoclonic bursts be classified (or clustered) automatically in EMG data.

Implementation and testing of suitable machine learning based methods. Available data: emg data, labeled myoclonic bursts             Here the goal would be to perform clustering or to train classifiers which discriminate between subtypes  of myoclonus and its neural origin (cortical or subcortical), based on identified and labeled myoclonus bursts.

C2) Classification or clustering movement disorders based on labeled data This project focuses on EMG and accelerometry data. These data are acquired  using a number of sensors placed on different body muscles. Using feature engineering and machine learning we try to find the relevant information that distinguishes one movement disorder from the other.  

Supervision: Elina van den Brandhof and Michael Biehl 

Level: Research Internship project or MSc project

Suitable for 1 or 2 students of CS (or possibly AI)          Research questions / tasks: (1)Review: Which supervised or unsupervised machine learning algorithms could be useful to cluster or classify movement disorders?          (2) Application: Can movement disorders be classified (or clustered) automatically in features extracted from EMG data? 

Implementation and testing of suitable machine learning based methods.                      

Available data: EMG and ACC data, labeled time windows (two seconds each)

D1) The Dynamics of Learning: Fixed Points Analysis

Context:

The dynamics of gradient based training of large layered neural networks from a stream of data can be studied analytically in model situations. In these so-called student teacher scenarios, the training is described in terms of coupled ordinary differential equations (ODE) for a set of characteristic quantities, termed order parameters in physics jargon. Integration of the ODE yields typical learning curves, e.g. the achieved accuracy or generalization error as a function of the number of update steps and/or examples in the training.

We have studied the learning dynamics in ReLU networks in comparison with the previously considered systems with sigmoidal activations, see e.g. [1] and [2].

Research questions / tasks:

Typically, the dynamics displays so-called plateau states, in which progress is very slow before the quasi-stationary plateau is eventually left. Plateaus are associated with zeros of the ODE system which correspond to weakly repulsive fixed points of the dynamics. In practice, the existence of plateaus is inevitable and can cause significant delay in the training processes.

In this project the goal is to identify all potential plateau states and their mathematical properties in a given model situation. Emphasis will be on the influence of the learning rate on the number of plateaus / fixed points. An important question is their dependence on the activation functions used in the trained networks.

After some literature study and acquisition of an understanding of the formalism, the main part of the project will concern the numerical search for zeros of the multi-dim. system of ODE and their interpretation. Their mathematical properties will be investigated with respect to their influence on the training dynamics. At first, a systematic comparison between sigmoidal and ReLU activation functions will be aimed at. To this end, we will first consider learning in the limit of small learning rates, which simplifies the analysis. If time allows, the studies will be extended to finite learning rates and other activation functions.

Supervision: Michael Biehl, Frederieke Richert, Otavio Citton

References: [1] M. Biehl, P. Riegler, C. Wöhler. Transient dynamics of on-line learning in two-layered neural networks. Journal of Physics A, 1996

[2] M. Straat, M. Biehl. On-line learning dynamics of ReLU neural networks using statistical physics techniques. Proc. European Symposium on Artificial Neural Networks (ESANN), 2019

[3] O. Citton, F. Richert, M. Biehl. On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions. Proc. European Symposium on Artificial Neural Networks (ESANN), 437-442, 2024

[4] O. Citton, F. Richert, M. Biehl. Phase Transition Analysis for Shallow Neural Networks with Arbitrary Activation Functions. Available at SSRN: https://ssrn.com/abstract=5006954

D2) Monte Carlo Simulations for Equilibrium States of Shallow Neural Networks

Context:

The batch learning of layered neural networks, where the network gets access to the training data set all at once, can for model neural networks be described in a statistical mechanics framework. This allows us to discern typical generalization behavior of these networks in the form of learning curves (generalization error as function of training set size) and gain insights into the theory of neural networks. We are in particular interested in analyzing the influence of different activation functions on the generalization behavior of neural networks and have in recent research advanced methods to allow for this analysis, see e.g. [1], [2] and [3].

Research questions / tasks:

The description of batch learning is assuming that, given some conditions from statistical mechanics are met, the network will undergo a gradient descent and will end up in an equilibrium state given by the Gibbs-Boltzmann distribution. Depending on further simplifying assumptions it can be more or less difficult to extract information from this distribution for a theoretical description of the equilibrium state of the network. In either case it is crucial to test theoretical results with simulations.

After some literature study and acquisition of an understanding of the formalism, the main part of the project will concern performing Monte Carlo simulations for backing up theoretical results about batch learning of neural networks. This will involve networks with different activation functions.

Furthermore an analysis of different training temperatures, equivalent to stochasticity in the training, will be investigated. If time allows, studies might be extended to other network architectures, such as the autoencoder. Depending on the number of students taking on projects, a collaboration between projects might also be possible.

Supervision: Michael Biehl, Frederieke Richert, Otavio Citton

D3) The Role of the Activation Function in Shallow Autoencoders

Project Description:

Neural Networks have recently gained the spotlight in the ongoing Artificial Intelligence revolution. However, the huge developments in the realm of applications have not been accompanied by theoretical understanding. In this project we propose an analysis of shallow autoencoder architectures in the framework of statistical physics, a tool that has proved to be of great value and from which much of our theoretical understanding was derived.

In [1], the authors show the different dynamical behavior presented by autoencoders with sigmoid and ReLU activation functions. By applying the tools developed in [2,3] for the analysis of Soft Committee Machines, this project aims to obtain further insight on the role of the activation function in equilibrium and dynamical settings.

In the framework of statistical physics, these systems are analyzed in the student teacher scenario, and the dynamics is described using ordinary differential equations (ODEs) for the order parameters, quantities that describe the macroscopic behavior of the network. The work here is to obtain the appropriate ODEs and solve them numerically under different conditions, and perform an analysis on the impact that the choice of the activation function has in the learning curves. To verify the results, we compare our predicted results with stochastic gradient descent simulations. For the equilibrium analysis (or batch learning), the objective is to describe the equilibrium distribution over the loss landscape after very long training time. This description is usually done using equilibrium statistical mechanics, where the goal is to compute a free energy and state equations that tell us how the equilibria change with the control parameters such as training set size.

Moreover, if everything goes smoothly and time allows, a collaboration with students working with Monte Carlo simulations is possible and could be interesting to enrich both projects and create a more solid ground for the results of the project.

Supervision: Michael Biehl, Frederieke Richert, Otavio Citton

References:

[1] M. Refinetti, S. Goldt. The dynamics of representation learning in shallow, non-linear autoencoders. Proceedings of Machine Learning Research, PMLR 162:18499-18519, 2022

[2] O. Citton, F. Richert, M. Biehl. On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions. Proc. European Symposium on Artificial Neural Networks (ESANN),437-442,2024

[3] O. Citton, F. Richert, M. Biehl. Phase Transition Analysis for Shallow Neural Networks with Arbitrary Activation Functions. Available at SSRN:https://ssrn.com/abstract=5006954

LSBGnet (Su, et al, 2024) is a recently published deep network aimed at finding low-surface-brightness (LSB) galaxies in particular in large surveys. Although the original paper does compare the performance of this network to one classical source detector (Sextractor, Bertin & Arnouts 1996), and several deep networks, it does not compare the method to a state-of-the-art faint object detector MT-Objects (or MTO, Teeninga et al, 2016), which came out best i a recent comparison of classical tools (Haigh et al, 2021).

The aim of this project is to make a thorough comparison of LSBGnet to MTO, using several quality criteria. An important difference between LSBGnet and the likes of MTO is that the latter aim to detect all the objects, not just the LSB objects. This must be addressed in the comparison.

References

E. Bertin, S. Arnouts, Astron. Astrophys. Suppl. S. 117, 393 (1996)

Haigh et al, Astronomy & Astrophysics 645, A107 (2021)

Su et al. MNRAS 528, 873–882 (2024)

Teeninga et al. Mathematical Morphology - Theory and Applications 1 (1), 100–115 (2016)

MTO is an astronomical source-finding tool based on max-trees and the use of chi-squared statistics. It has shown great performance for images in which the number of photons detected per pixel is high enough to allow the Poisson noise in the signal to be approximated by a Gaussian distribution with variance scaling linearly with the mean. In certain imaging modalities, such as in STED microscopy, photon counts are far too low to make this work reliably. There are several better statistical tests that could be used in this situation, but such an adaptation has yet to be realised.

The aim of this project is to explore suitable statistical tests, and test this on real and simulated STED microscopy data.

Number of positions: 5

Within the DigiAgro3 project, we are looking at methods for detection of plant disease for crop monitoring. A database of images and labels in the form of bounding boxes is available to train and test ML techniques for this task. The aim of this project is to implement different types of feature extraction methods, including texture, colour, and morphological profiles, and optimizing dissimilarity measures and machine learning approaches to differentiate healthy from diseased leaves, using as little training data as possible. In this project, up to five students can participate, each choosing a particular set of methods to implement and test. At the end, a full-blown comparison will be performed.

Num of positions: 2

MNNs are fairly recent variants of convolutional neural networks (CNNs), in which the convolutional layers are replaced by morphological operators, i.e. dilations and erosions. The idea is that whereas CNNs can approximate any continuous function, given enough layers and training data, MNNs are better at approximating discontinuous functions. In theory, this might require fewer layers and training examples. The aim of this project is to implement and test MNNs on selected standard image recognition data sets, in comparison to CNN counterparts. Furthermore, hybrid CNN/MNN networks might be able to make use of the strengths of both methods. Different ways of combining these networks can be explored in this project.

Num of positions: 2

Within the DigitAgro3 project a data set has been created using a drone flying over a field. The aim is to detect plant stress in the field using multi-spectral data collected with the camera on the drone. In this project you will explore a number of different image analysis methods to find diseased plants, or plants that lack nutrients. Methods to be explored include so-called alpha trees and multi-spectral texture methods. Ideally, the system would require very little in the way of annotated data.

Num of positions: 3

A hyper-spectral image data set is being gathered, aimed at determining different plant stress factors, such as disease, lack of nutrients, drought, salinity, etc. Unlike multi-spectral images, which have between 4 and a few tens of spectral bands, hyperspectral images can have many hundreds of spectral channels, each less than one nanometer wide. One could say each pixel contains a function, rather than a vector of a few features. The aim of this project is to explore methods of identifying stress factors using this fine-grained spectral information. Furthermore, we want to develop optimal dissimilarity measures to segment such images using alpha trees. Finally, we want to simulate multi-spectral data, by binning the hyperspectral data. This way, we could investigate what minimal set of of spectral bands would be optimal for this kind of stress detection.

To help find extended sources in astronomical images, it is often necessary to remove point sources. This is usually done by determining the point-spread function (PSF) of the optics, finding objects for which the brightness profile fits, and subtracting a scaled version of the PSF at that point. The aim of this project is to adapt the Max-Tree-Objects (MTO) object detection algorithm to perform this task. Point sources should show up in the max-tree of an image as branches of the tree with a distinctive morphological profile. By finding significant objects that fit the profile of a point source of a given magnitude, removing them reduces to pruning these branches from the tree. This yield a catalogue of point sources with positions and magnitudes, and it would make detection of faint, extended sources easier. What could be done as an extension is to find regions with an over-density of point sources. If these are associated with an extended source (as indicated by the parent-child relationship in the tree), it might be possible to detect globular cluster around distant galaxies, or distant galaxy clusters.

Number of positions: 5

We have a few vacancies for projects on building high-performance and trustworthy autonomy in mobile robotics. The topic lies within but not restricted to the following domains:

(1) Dynamic state estimation using multimodal sensors such as inertial measurement units, light detection and ranging (LiDAR) sensors, RGB(-D) cameras, ultra-wideband (UWB) sensors, dynamic vision sensors, etc.

(2) Mobile scene mapping and understanding on smart edges

(3) Motion planning and locomotion of quadrupedal robots

(4) Collaborative multi-robot exploration

Our projects typically involve both algorithmic innovations and real-world validations. It is also possible that a project is theoretic-oriented. Each project will be tailored according to the student’s interests and skills. Students are also encouraged to come with their own ideas, and we are open to provide supervision. See more information here: https://asig-x.github.io/

Required:

(1) Solid programming skills in C++, Matlab or Python, pre-knowledge in ROS(2) is a plus

(2) Good at and comfortable with mathematics, happy to work on hardwares and “get hands dirty”

(3) Strong motivation and dedication in developing cutting-edge technologies, excellent communication skills, willing to work in teams

Contact me: Kailai Li (kailai.li@rug.nl)

No of positions: 2

This project explores the use of contrastive learning, a self-supervised learning technique aimed at extracting discriminative features to enhance image representation. The focus will be on a framework called Image BERT, which applies contrastive learning with original images and their masked versions, where specific regions of the images are removed. This approach aims to improve the model's ability to understand and represent visual data more effectively. Students participating in this project will work with Vision Transformers(ViTs). While prior experience with ViTs is not mandatory, a strong motivation to learn and explore them is essential. For detailed information and project ideas, please contact j.guo@rug.nl.

References:

[1] Zhou et. al, IBOT: IMAGE BERT PRE-TRAINING WITH ONLINE TOKENIZER, ICLR 2022.

[2] He et. al, Masked Autoencoders Are Scalable Vision Learners, CVPR 2022.

Status: Open

No. of positions: 3

Dis/misinformation, including fake news and hate speech, is prevalent on social media, particularly in the context of the Covid-19 pandemic, the Ukraine-Russia war as well as elections. While extensive research have been made in the textual analysis of disinformation, the impact of visual elements like images, videos, and memes in spreading misinformation remains less explored. These visual elements are potent in emotionally engaging and influencing public opinion, thereby exacerbating societal divisions and fueling community polarization. In this project, we will focus on multimodal analysis of disinformation that incorporates both visual and textual information of news or posts. Multiple possible directions can be offered depending on your interests. For more information, please contact: j.guo@rug.nl.

References: [1] Nakamura et al. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. https://arxiv.org/pdf/1911.03854v2.pdf  

[2] https://paperswithcode.com/paper/rfakeddit-a-new-multimodal-benchmark-dataset/review/

Status: Open

No. of positions: 2

The common practice for machine learning applications is to feed as much data as the model can take. This is because in most machine learning applications feeding more data enables the model to predict better. However, few-shot learning aims to build accurate machine learning models with less training data. The metric-learning based few-shot image classification focuses on learning a transferable feature embedding network by estimating the similarities between query images and support classes from very few images.

In this project, we aim at improving the current few-shot learning approaches for image or video classification. TWO projects can be offered under image classification, one focuses on channel attention and the other on incorporating frequency information. One project is offered under video classification with a focus on vision transformer. For more information, please contact: j.guo@rug.nl.

References:

[1] Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo. Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning. https://arxiv.org/abs/1903.12290

[2]Hiller et al. (2022) Rethinking Generalization in Few-Shot Classification. NeurIPS.

[3] Cheng et al. (2023). Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

[4] D’Alessandro et al. (2024). Multimodal Parameter-Efficient Few-Shot Class Incremental Learning.

Status: Open

No. of positions: 1

In this topic, students will research NN architecture induced bias using information theoretic tools. Specifically, they will measure the Mutual Information (MI) between representations of varying architectures trained for the same task (e.g., ResNets/ConvNeXts) Mutual Information (MI) is an information theoretic measure that quantifies the amount of information obtained about one random variable through another random variable. In other words, it measures how much knowing one of these variables reduces uncertainty about the other. For two random variables X and Y, the mutual information I(X;Y) is defined as the difference between the entropy H(X) and the conditional entropy H(X?Y): I(X;Y)=H(X)?H(X?Y) Notably, mutual information can measure nonlinear relationships, unlike correlation coefficients, which are limited to linear relationships. Answering the questions may lead to insights for knowledge distillation (KD), Dynamic Deep Neural Networks (DDNNs), Modular Deep Learning (MDL), and to architectural designs.  

Research Tasks:

• Study Information Theory:

    * Gain an understanding of information theory basics, and its application to modern deep learning (DL) methods. 

• Implement or port existing Mutual Information Estimation methods (At least MINE)

    * Gain an understanding into the state-of-the-art in MI estimation

• Conduct experiments to answer research questions

Possible research questions:

• What is the relationship between I(X, Rn) and classification accuracy?

    * Given several n-layered Artificial Neural Network (ANN) that achieve varying Top-k accuracy on the same task, measure whether they retain comparable information.

• What is the MI between two related architectures (E.g., ResNet-18 and ResNet-50) of representations at the same and at the last layer index?

    * Do shallower networks discard information more aggressively/conservatively than deeper networks, or is the information comparable at the last layer before the classifier?

• What is the MI between two unrelated architectures of representations at the same and the last layer index?

    * Like above, but focus more on architecture induced bias. 

References:

• Cover, T. M. (1999). Elements of information theory. John Wiley & Sons.
• Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., & Hjelm, R. D. (2018). Mine: mutual information neural estimation. arXiv preprint arXiv:1801.04062.
• Shwartz-Ziv, R., & Tishby, N. (2017). Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810.

For further information contact the CS or TU Wien supervisor: ?Kerstin Bunte (k.bunte@rug.nl) or Alireza Furutanpey (a.furutanpey@dsg.tuwien.ac.at) 

Status: Open

No. of positions: 2

Unlike conventional centralized approaches, Federated Learning (FL) aggregates model updates from multiple devices or servers without requiring direct access to the underlying data [1]. The primary motivation is that sensitive data is scarce and cannot be shared for legal or ethical reasons (e.g., medical records).  The idea is that since locally available data may not train a prediction model with sufficient performance, organizations can share data securely in federations. However, considering the sheer amount of work that is output that does not solve the problem FL is meant to, there are reasons to doubt the underlying approach. Compared to fundamental research in machine learning, deep learning, computer vision, and natural language processing, FL is a niche research area. While it addresses an essential problem, it only serves a specific purpose.  Yet, for 2023 alone, the title-name query ‘’ federated learning survey`` in the ACM digital library returns around 7000 hits. For reference, this is roughly the same number of hits when querying ‘’deep learning survey`` (6924) or ‘'machine learning survey`` (6824),  and even 2000(!) more than “Computer Vision survey``(4304). Therefore, we ask ourselves whether Federated Learning is worthwhile as an area of research and observe parallels to past efforts in Object-relational mapping (ORMs), commonly titled the “Vietnam of Computer Science [2].” We argue that FL is similarly the “Vietnam of Machine Learning,” where it represents a dilemma that starts well, gets more complicated as time passes, and before long entraps researchers in a commitment that has no clear demarcation point, no clear win conditions, and no clear exit strategy. Analogous to how doubts on the war’s motivation increasingly surfaced, FL's typical justification (i.e., data scarcity due to privacy concerns and hard-to-obtain data) may increasingly not hold.  Notably, methods in Self-Supervised Learning (SSL), Knowledge Distillation (KD), and data synthesis have significantly advanced in recent years. While the solution approach is orthogonal to FL, they apply precisely to the problem FL attempts to solve, i.e., aid organizations in training reliable prediction models with scarce data.  In this topic, students will focus on a particular frontier of FL research and how well the motivations align with the solution approach. This topic is conductible in pairs, focusing on distinct areas that should complement each other.  Since the topic covers a wide area, the student will first read into FL (see survey in references) to gain a basic understanding. Then, we will scope the project and define tasks/research according to their interests.

References:

• Neward, T. (2006). The Vietnam of computer science. The Blog Ride, Ted Newards Technical Blog.
•  Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2023). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513-535.

For further information contact the CS or TU Wien supervisor: Kerstin Bunte (k.bunte@rug.nl) or Alireza Furutanpey (a.furutanpey@dsg.tuwien.ac.at)

Status: Taken

No. of positions: 3

Learning in the Model Space?Time series data emerges in virtually all scientific sectors and also plays an important role in modern medicine. Medical data tend to be irregularly or sparsely sampled and typically contain noise. These limitations in the amount and quality of data make classification using traditional machine learning (ML) methods difficult. However, if the process that produces the time series data can be explained by a dynamical system, then a mechanistic model can be introduced to incorporate domain knowledge, effectively guiding the ML process and making the learned outcomes interpretable.?A framework which is particularly suited for this job is Learning in the Model Space (LiMS). Instead of performing classification on the time-series directly, a posterior distribution is constructed for every time series, which quantifies the level of belief for every possible parameterization (realization) of the given mechanistic system. Obtaining this distribution is typically not analytically tractable, so the posterior has to be approximated using sampling techniques.

Sampling Methods?In the context of Bayesian inference, sampling involves drawing random samples from the posterior distribution of a model. These samples are used to approximate complex integrals, enabling the estimation of model parameters and making predictions. Several sampling methods have been developed to tackle Bayesian inference problems. Notable techniques include:

• Markov Chain Monte Carlo (MCMC): MCMC methods involve generating a Markov chain of samples, which converges to the target posterior distribution. Two MCMC variants that are particularly relevant are:

    * Parallel Tempering MCMC: This method introduces multiple chains with different temperatures to improve exploration of the parameter space.
    * Hybrid Monte Carlo MCMC: Hybrid Monte Carlo combines Hamiltonian dynamics and MCMC to enhance the efficiency of sampling.

• Nested Sampling: Nested Sampling is an alternative method that explores the posterior distribution by iteratively constructing a sequence of nested likelihood-weighted distributions.

Research Question ?Which sampling method is most suitable for Learning in the Model Space applications?

Concrete Research Tasks

• Learn about the LiMS Framework:

    * Gain a comprehensive understanding of the Learning in the Model Space (LiMS) framework and its importance in Bayesian inference.

• Study State-of-the-Art Sampling Methods:

    * Investigate state-of-the-art sampling methods, including Parallel Tempering MCMC, Hybrid Monte Carlo MCMC, and Nested Sampling.
    * Utilize research papers by Ballnus et al. and the review by Buchner as starting points for in-depth exploration.

• Determine the Most Suitable Sampling Technique:

    * Implement state-of-the-art sampling methods. Core functionalities preferably implemented in C/C++ with a Matlab or Python wrapper.
    * Evaluate and compare the effectiveness and efficiency of the sampled techniques in the context of LiMS applications.
    * Identify the sampling method that best suits the unique requirements of LiMS.

Depending on the level of the student (BSc/MSc), the scope of the project is flexible. Whether the project focuses on contributions on the theoretical side or more on implementation is up to the student’s personal preference.

References

• A classification framework for Partially-observed Dynamical Systems 
• Nested Sampling methods
• Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems

How to apply for this project??For further information contact: Kerstin Bunte (k.bunte@rug.nl), Elisa Oostwal (e.c.oostwal@rug.nl) or Janis Norden (j.norden@rug.nl).

Status: Taken

No. of positions: 3

Pediatric pulmonary hypertension (PH) is a rare disease and defined by an increased pulmonary arterial pressure. Based on pathophysiological mechanisms, clinical presentation, and hemodynamic characteristics, PH can be classified into five main diagnosis groups: pulmonary arterial hypertension (PAH, group 1), PH due to left heart disease (group 2), PH due to lung disease and/or hypoxia (group 3), PH due to pulmonary artery obstructions (group 4), and PH with unclear and/or multifactorial mechanisms (group 5). Each PH type can be further divided into multiple diagnosis subgroups.

Group 1 PAH is a progressive and eventually fatal pulmonary vascular disease. The introduction of PAH-targeted therapies in the past two decades has improved survival in children and adults with PAH, but prognosis is still poor. Once a patient is in end-stage disease, lung transplantation is the only remaining treatment option. Current treatment strategies are guided by risk stratification, where the patients are categorized as having low, intermediate or high risk for mortality with the aim to achieve and maintain a low-risk status. The estimated risk is based on multiple clinical, hemodynamic, and echocardiographic parameters with their own cut-off values for each risk category. However, the prognostic ability of current risk stratification models in adult patients is moderate at best, and research supporting the use of risk stratification in children with PAH is scarce.

In the Netherlands, all children with PH are referred to the national referral center for pediatric PH in Groningen, where they are diagnosed, treated and followed according to standardized formats for over 20 years now. The Dutch National Registry for Pulmonary Hypertension in Childhood systematically collects data of these patients at set follow-up time points, including clinical presentation, symptoms, physical examination, genetic analysis, biochemical biomarkers, ECG, echocardiography, MRI, cardiac catheterization data, exercise performance, accelerometry, treatment strategies and outcome. Using machine learning techniques, we want to find hidden patterns within these registry data, specifically focusing on 1) searching for clusters within the data that may serve as disease phenotypes, determining whether these phenotypes improve the current classification of PH patients, and how they relate to outcome and 2) generating a self-learning predictive model to evaluate disease progression, treatment response and prognosis in pediatric PAH patients. With the resulting models we hope to create the gateway to an evidence based personalized treatment approach for pediatric PH and, ultimately, to improve the outcomes of these children.

The dataset

The Dutch National Registry for Pulmonary Hypertension in Childhood will be used for data analysis. This registry contains data systematically collected over the last 20 years from around 250 consecutive children with pulmonary hypertension. The registry includes over 3500 follow up moments with a mean follow up time of 5 years, which add up to 1100 patient follow-up years. The data collected include individual patient data regarding clinical presentation, diagnostic work-up, diagnostic classification (etiology), genetic test results, treatment and follow-up. Follow-up data, collected every 3 to 6 months, contain biometric and clinical data such as physical examination data, treatment data, exercise test results including six-minute-walking-distance and accelerometry, laboratory test results (Including biochemical markers for heart failure such as NT-proBNP), ECGs, echocardiographic data (measurements and raw data). Data indicating disease progression such as hospitalizations, treatment escalations, the need for catheter interventions or lung transplantation, and death are also included in the database. Due to the grim prognosis of pediatric PAH with a 5 year survival rate of 65%, almost 100 outcome events such as deaths or lung transplantations have been registered. To summarize, in total the registry includes more than 150,000 data points, and in addition raw ECG, raw echocardiographic and raw accelerometric data.

Expectations

This project is a first exploratory data analysis of a novel collaboration. Therefore, the directions are multitude. If you are interested in biomedical data analysis and have a good understanding of general supervised and unsupervised machine learning techniques, as well as fun potentially going deeper into mathematical concepts and not scared by exploring novel directions independently, this project might be for you!??

How to apply for this project?

This is a collaboration project and hence we have a procedure of application. Submit the following:? 1) a short motivation letter of why you are the best student to take up this project (max 2 pages)? 2) a short CV/course grades, so we can see your background knowledge (max 2 pages)? After we viewed the material we will invite suitable candidates for a short interview.?For further information contact the supervisors: ?CS: Kerstin Bunte (k.bunte@rug.nl) or Elisa Oostwal (e.c.oostwal@rug.nl) or  UMCG: Chantal Lokhorst (c.lokhorst@umcg.nl).

Status: Taken

No. of positions: 1

Pulmonary hypertension (PH) is a rare disease and defined by an increased pulmonary arterial pressure. Based on pathophysiological mechanisms, clinical presentation, and hemodynamic characteristics, PH can be classified into five main diagnosis groups. Group 1 PAH is a progressive and eventually fatal pulmonary vascular disease. The introduction of PAH-targeted therapies in the past two decades has improved survival in children and adults with PAH, but prognosis is still poor. Once a patient is in end-stage disease, lung transplantation is the only remaining treatment option.

In the Netherlands, all children with (suspected) PH are referred to the national referral center for pediatric PH in Groningen, where they are diagnosed, treated and followed according to standardized formats for over 20 years now. To diagnose PH the patient has to undergo right heart catheterization (RHC). With this invasive procedure, a catheter is guided to the right side of the heart and into the pulmonary artery. Along the way, multiple pressure curves are obtained. An example of one of these pressure curves is given in the figure below. The three signals at the top (I, II, and III) are ECG signals, and the one at the bottom is the pressure curve measured in the left pulmonary artery (PA L). The x-axis shows the time in seconds (at the top) with a paper speed of 25 mm/s and the y-axis gives the pressure in mmHg. The values on the axis can differ for each graph/image and the images are screen shots (png-files), so the pixel position of the axis may differ as well.

The aim of this project is to convert the images of the RHC pressure curves into digital signals.

The end product should be able to:

1. Recognize the values on the x and y-axis.

2. Generate the signal trace.

3. Get the numerical values from the pressure plots and validate these with given values at the bottom of the plot.

4. Output of the signal as an excel file with two columns, one for time (s) and one for pressure (mmHg), and with a high sample frequency.

5. Optional: Split the signal in epochs based on the heart cycle using the RR interval. In the figure above, one RR interval is marked by blue lines as an example.

Challenging aspects include overlapping signals and multiple pressure curves in one image which should be separated.

For further information contact the CS or UMCG supervisor: Kerstin Bunte (k.bunte@rug.nl) or Chantal Lokhorst (c.lokhorst@umcg.nl).

Status: Taken

No. of positions: 2

The company Circ is the pioneer of the BioTransformer, a machine with a footprint of about 4m2 that transforms biowaste, such as leftover food and cutting waste, into a renewable source of biogas. BioTransformers are 'smart', in the sense that they are connected to the internet to collect sensor information and monitor and control the process remotely. This allows Circ to roll out machines across the country. Customers can also see production statistics in a mobile app.

Since its initial release, the user interface at the front of the BioTransformer has received few updates, and is in dire need of a redesign. The user interface is the primary way for the user to interact with, control, and fill their BioTransformer, and is vital to operate smoothly and intuitively. Moreover, customers have asked for a range of features that should be considered for implementation. Lastly, the UI is connected to over 100 sensors, relays, and actuators. These factors make designing a suitable UI quite a challenge.

The project Circ presents, comes in 3 phases

1. Retrieving stakeholder wishes as short user stories and requirements, and briefly analyse the feasibility of planned features. 2. Drafting and designing an interface that meets the requirements. 3. Setting up hooks to connect to the underlying control model. 4. The deliverables of the project include

1. A software package containing a user interface written in Qt5. 2. A short and concrete analysis of stakeholders, requirements, user stories, and design considerations. 3. A demonstration of the user interface (if time allows, preferably on location) A satisfactory product demonstration will lead to the deliverable being used in developing the next major version of the BioTransformer. For further information, please do not hesitate to reach out to Robbin de Groot (r.degroot@circ.energy). For more information about Circ and their BioTransformer, see https://circ.energy.

Status: Taken

No. of positions: 1

African clawed frog (Xenopus laevis) is a commonly used model organism for cell biological, developmental, and biomedical research. In the laboratory setting, frog colonies are generally housed in aquatic tank systems, usually in groups of ten to twenty individuals per tank. For health monitoring and experimental quality control purposes, it is desirable to identify individual frogs regularly throughout their life. Recently, we have developed a novel pipeline for data acquisition, pre-processing, and training of a classification model based on the recognition of the biometric pattern these frogs show on their backs (Prins et al., 2023). 

To make this tool available to the larger research community, in this project you will develop a web-based API which integrates the developed algorithm and provides users with the options to either train the model with their own frogs/colony, or apply the model to identify frogs within a colony. The frog ID should then be connected to a user-friendly database implementation that allows for the input/output of research relevant data (e.g., location/tank number, health history, experimental outcome parameters) to facilitate reusable and sustainable data management (DMP). For further information contact the supervisors: Kerstin Bunte (k.bunte@rug.nl) and Dario Tomanin (d.tomanin@rug.nl) PhD student in the Kamenz Lab, part of the Molecular Systems Biology group.

Status: Open

MTO is an astronomical source-finding tool based on max-trees and the use of chi-squared statistics. It has shown great performance for images in which the number of photons detected per pixel is high enough to allow the Poisson noise in the signal to be approximated by a Gaussian distribution with variance scaling linearly with the mean. In certain imaging modalities, such as in STED microscopy, photon counts are far too low to make this work reliably. There are several better statistical tests that could be used in this situation, but such an adaptation has yet to be realised. The aim of this project is to explore suitable statistical tests, and test this on real and simulated STED microscopy data.

Status: Open

Comparison of LSBGnet to MTO for finding low-surface-brightness galaxies LSBGnet (Su, et al, 2024) is a recently published deep network aimed at finding low-surface-brightness (LSB) galaxies in particular in large surveys. Although the original paper does compare the performance of this network to one classical source detector (Sextractor, Bertin & Arnouts 1996), and several deep networks, it does not compare the method to a state-of-the-art faint object detector MT-Objects (or MTO, Teeninga et al, 2016), which came out best i a recent comparison of classical tools (Haigh et al, 2021).

The aim of this project is to make a thorough comparison of LSBGnet to MTO, using several quality criteria. An important difference between LSBGnet and the likes of MTO is that the latter aim to detect all the objects, not just the LSB objects. This must be addressed in the comparison.

References

E. Bertin, S. Arnouts, Astron. Astrophys. Suppl. S. 117, 393 (1996)

Haigh et al, Astronomy & Astrophysics 645, A107 (2021)

Su et al. MNRAS 528, 873–882 (2024)

Teeninga et al. Mathematical Morphology - Theory and Applications 1 (1), 100–115 (2016)

Status: Open

The new WEAVE astronomical instrument (Dalton et al, 2018) is an imaging spectrometer, capturing data cubes of quite low spatial resolution, but very high spectral resolution. Rather than just having red, green and blue data per pixel, each pixel contains two spectra of about 4000 spectral channels each. This allows the study of astronomical objects such as interacting galaxies in unprecedented spectral detail. Motions of ionized gas within the structures can be mapped clearly, and compositions of stellar populations can be estimated. One problem in these data is the presence of cosmic ray events, which show up as bright spikes in the data cubes. The aim of this project is to detect these events, and remove the resulting spikes from the data. The basic tool that will be used is MTObjects (Teeninga et al, 2016, Haigh et al, 2021), which is a powerful source detector for optical data.

References: Dalton, G., Trager, S., Abrams, D. C., Bonifacio, P., Aguerri, J. A. L., Vallenari, A., Middleton, K., Benn, C., Dee, K., Sayède, F., Lewis, I., Pragt, J., Picó, S., Walton, N., Rey, J., Allende, C., Lhomé, É., Terrett, D., Brock, M., ... Jin, S. (2018). Construction progress of WEAVE: The next generation wide-field spectroscopy facility for the William Herschel Telescope. In C. J. Evans, L. Simard, & H. Takami (Eds.), Proceedings Volume 10702, Ground-based and Airborne Instrumentation for Astronomy VII; 107021B (Vol. 10702). [107021B] SPIE.Digital Library. https://doi.org/10.1117/12.2312031 Haigh, C., Chamba, N., Venhola, A., Peletier, R., Doorenbos, L., & Wilkinson, M. H. F. (2021). Optimising and comparing source-extraction tools using objective segmentation quality criteria. Astronomy & astrophysics, 645(January 2021 ), [A107]. https://doi.org/10.1051/0004-6361/201936561 Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. (2016). Statistical attribute filtering to detect faint extended astronomical sources. Mathematical Morphology - Theory and Applications, 1(1), 100–115. https://doi.org/10.1515/mathm-2016-0006

Status: Open

In microbiology, tracking dividing yeast cells in time series imaging is a tedious task, and some results in automatic this task have been obtained using deep learning. However, it is difficult to get sufficient ground truth data for training, and the method does not yield good results on complicated cell shapes. The aim of this project is to explore classical morphological image processing tools, to circumvent these problems. It is also possible to combine these morphological methods with deep neural networks.

References: TO DO

Status: Taken

Nuwa Pen is the world's first smart ball point pen. Nuwa pen has successfully captured the essence of digital writing without making any compromises to the true writing experience of a ballpoint pen on any piece of paper. Nuwa Pen works with cutting edge processing power along with a suite of sensors which helps the pen figure out what the user is writing and where the user is writing. Nuwa Pen is developed by Nuwa Pen B.V. Nuwa Pen B.V. is a start-up based out of Groningen, The Netherlands. The aim of Nuwa Pen B.V is to combine the analogue world with the digital world with the motto of Making the World your canvas. At Nuwa Pen B.V. we want to revolutionize how humans write and interact with the digital world.

In this research project, you will be working on designing a binarization algorithm in C++ for low-resolution, gray-level multichannel video sequence. The binarization has to use adaptive threshold and be consistent over different channels and timestamps. The algorithm has to be highly optimized, and has to be written from scratch without using any 3rd party library, based on the rudimentary binarization algorithm implemented in our proprietary codebase. You will be working as a member of our software engineering team who you can share your idea, get support, and collaborate using Github.

Status: Taken

Nuwa Pen is the world's first smart ball point pen. Nuwa pen has successfully captured the essence of digital writing without making any compromises to the true writing experience of a ball point pen on any piece of paper. Nuwa Pen works with cutting edge processing power along with a suit of sensors which helps the pen figure out what the user is writing and where the user is writing. Nuwa Pen is developed by Nuwa Pen B.V. Nuwa Pen B.V. is a start-up based out of Groningen, The Netherlands. The aim of Nuwa Pen B.V is to combine the analogue world with the digital world with the moto of Making the World your canvas. At Nuwa Pen B.V. we want to revolutionize how humans write and interact with the digital world.

In this research project, you will be working on designing skeletonization algorithm in C++ for low-resolution images. The image skeleton has to have sub-pixel accuracy due to low image resolution, and has to be robust to image noise. The algorithm has to be highly optimized, and has to be written from scratch without using any 3rd party library, based on the rudimentary skeletonization algorithm implemented in our proprietary codebase. You will be working as a member of our software engineering team who you can share your idea, get support, and collaborate using Github.

Status: Open

No. of positions: 3

The common practice for machine learning applications is to feed as much data as the model can take. This is because in most machine learning applications feeding more data enables the model to predict better. However, few-shot learning aims to build accurate machine learning models with less training data. The metric-learning based few-shot image classification focuses on learning a transferable feature embedding network by estimating the similarities between query images and support classes from very few images.

In this project, we aim at improving the current few-shot learning approaches for image or video classification. TWO projects can be offered under image classification, one focuses on channel attention and the other on incorporating frequency information. One project is offered under video classification with a focus on vision transformer. For more information, please contact: j.guo@rug.nl.

References: [1] Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo. Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning. https://arxiv.org/abs/1903.12290

[2] Hu et al. (2017). Squeeze-and-Excitation Networks. CVPR. https://arxiv.org/abs/1709.01507

[3] Cheng et al. (2023). Frequency Guidance Matters in Few-Shot Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

Status: Open

No. of positions: 3

Dis/misinformation, including fake news and hate speech, is prevalent on social media, particularly in the context of the Covid-19 pandemic and the Ukraine-Russia war. While extensive research have been made in the textual analysis of disinformation, the impact of visual elements like images, videos, and memes in spreading misinformation remains less explored. These visual elements are potent in emotionally engaging and influencing public opinion, thereby exacerbating societal divisions and fueling community polarization. In this project, we will focus on using deep learning, e.g. convolutional neural networks, vision transformers, or even diffusion models, for the visual analysis of disinformation. Multiple possible directions can be offered depending on your interests. For more information, please contact: j.guo@rug.nl.

References: [1] Nakamura et al. r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. https://arxiv.org/pdf/1911.03854v2.pdf  

[2] https://paperswithcode.com/paper/rfakeddit-a-new-multimodal-benchmark-dataset/review/

Context

Filamentary structures (one-dimensional manifolds) are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. 1-DREAM is a toolbox composed of five main Machine Learning methodologies whose aim is to facilitate manifold extraction. Problem

The toolbox has been published and tested on several problems. However, its use is limited by the size of the input data, and its historically grown distributed implementation makes it not too user-friendly. Furthermore, despite showing better results than other tools dedicated to the same task, 1DREAM is not as well known by the scientific community as they are.

Goal

The international team of 1DREAM is looking for an efficient implementation of the current code in C++ with a Python interface to make it faster and more user-friendly. Moreover, we are eager to make this tool more widely known to the scientific community improving the web page and the associated documentation. Depending on the background of the student and type of project taken, also algorithmic improvements to ML algorithms within 1DREAM are possible.

Assignment

The team of 1DREAM wants to pass the current code written on Matlab and Python to an optimum design written in C++ wrapped by Python. Moreover, we are looking for a student who can help us in making the tool more user-friendly and more efficient. The student (MSc/BSc) is given the assignment to do research on which code design is most suitable for doing this. The student can then use the current datasets provided by the 1DREAM team to carry out experiments with the various designs to achieve good performance. The student must create the documentation necessary to make 1DREAM more user-friendly. Finally, the student is motivated to show ideas about the performance of 1DREAM and/or the design of a better website for the tool.

How to apply and/or get more information

Please prepare a short 1-2-pages CV and 1-page motivation letter to apply for this project and indicate if you are looking as a Master or Bachelor student. Several supervision slots are available. Contact information below: Contact: Felipe Contreras and Kerstin Bunte E-mail: f.i.contreras.sepulveda@rug.nl and k.bunte@rug.nl

Related literature

https://doi.org/10.1016/j.ascom.2022.100658 https://doi.org/10.1093/mnras/stad428 https://doi.org/10.1109/TKDE.2022.3177368 https://doi.org/10.1162/neco_a_01478 https://doi.org/10.1016/j.artint.2021.103579

This project is provided in collaboration with Repowered. Due to the technical nature of the energy system, electricity demand and supply have to be in balance at all times. If this is not the case, an energy imbalance is present on the electricity grid. The total imbalance on the grid is the aggregate of local imbalances of individual energy consumption and production assets such as solar parks. For example, a solar park causes imbalance when its actual production is unexpectedly higher than its nomination (the volume that has been sold on the day-ahead market based on production forecasts). This imbalance is (partially) negated for example if a wind park is producing less energy than nominated (forecasted) at the same time. However, in total there is always some imbalance present in the grid as a whole, especially as the amount of renewable energy assets (which are difficult to forecast) increases.

To counter imbalances on the grid, the Transmission System Operator (TSO; TenneT in the Netherlands) tries to encourage electricity market participants to change their production/consumption to restore the balance on the grid. Market participants that have an imbalance in the same direction as the current imbalance have to pay a penalty, which is then paid out to those that have an imbalance in the ounter-direction to the current imbalance. The penalty/compensation (the imbalance price) is determined per 15 minute block and is paid out per MWh of extra consumption/production relative to its nominated volume.

Solar parks can participate actively in this imbalance market by curtailing (shutting down) its production at selected times when a surplus of energy is present in the grid and the current imbalance price is below a certain threshold (i.e. to get paid to produce less electricity). The difficulty lies in choosing the right moment to start curtailing, since the imbalance price is only published after the 15 minute block in which the imbalance is measured. To this end, a price forecast is needed to determine whether to curtail the solar park during a certain time block or not. Repowered offers solar curtailment as one of its services to solar park owners, and is looking for ways to improve its services and further develop its know-how on price forecasts and AI models. This assignment entails the creation of a machine learning model for curtailment decision making. Because of the binary nature of the decision (to curtail or not), a binary classification model with time series input should be made. Furthermore, the assignment entails the validation of the model in the virtual production environment of Repowered. In a previous project, several regression models have been created to predict the imbalance price for the next 15 minute block. This has given some good insights for a continuation of the project and can be used as a starting point for data gathering and literature review.

For more information contact: Kerstin Bunte or Timo Dettmering <t.dettmering@newenergycoalition.org>

Autonomous navigation, although theoretically solved, is still problematic in practice. We investigate joining traditional approaches with Dimensionality Reduction Machine Learning as well as the combination of complementary sensors to achieve the task more efficiently and robustly. The project is flexible and can take multiple directions to choose from.

For more information contact: Kerstin Bunte

Related literature: https://www.sciencedirect.com/science/article/abs/pii/S0031320319304923

Nephrology specializes in the study of kidneys and medical issues concerning them. In collaboration with the UMCG we would like to analyze a multitude of problems and the potential use of computer aided diagnosis systems based on different data modalities that open the opportunity for different project directions. One of which is the use of data extracted from bodily fluids, such as urine, to detect abnormalities and biomarkers for certain conditions and another is the segmentation of functional MRI volume sequences to analyze the function of a kidney by detecting vessels and their streaming behavior over time. The hospital is also a well known center for kidney transplantation and is interested in early detection of problems after transplantation. Since this is a novel collaboration the direction of a potential project is flexible and exploratory to gain further insight into the problems faced in Nephrology. Email: k.bunte@rug.nl for further information

Context

Powerchainger is a startup based in Groningen. Our mission is to make the energy transition accessible and affordable for everyone. We believe that everyone has the right to clean energy and a sustainable living environment. By using energy data as a driver, we ensure energy-efficient households and more sustainable neighborhoods.

Problem

Powerchainger wants to investigate whether household appliance detection is possible using machine learning (ML). The consumption of electrical appliances is measured in kilowatt-hour (kWh) as a unit of energy. This is done with the help of a smart meter. Smart meters can now be found in nearly 90% of all Dutch households. The measured values that smart meters produce consist of the total consumption of all devices in the household. Because of that, we cannot initially see what this consumption consists of. Or rather: which specific or unique devices are consuming.

Goal

Powerchainger is looking for ways to detect near real-time which devices in the house are on (running and consuming), by using smart meter data. We are looking for possibilities to distinguish electrical appliances automatically based on their consumption. And to detect them and to be accurate as possible. If this is successful, it offers opportunities to give households real-time feedback about their energy consumption. And to reward energy-efficient behavior.

Assignment

Powerchainger wants to know if it is possible to use a machine learning model to detect which devices in the house are running. This model can be trained with measured values from the past, labeled with the composition of devices that have produced these measured values. The student (MSc/BSc) is given the assignment to do research on which machine learning models are most suitable for doing this. The student can then use a dataset provided by Powerchainger to carry out experiments with the various models to arrive at sound advice. The emphasis is on (applied) research related to ML. Backend development skills are a plus. How to apply and/or get more information Please prepare a short 2 page CV and 1 page motivation letter to apply for this project and indicate if you are looking as a Master or Bachelor student. Several supervision slots are available. Find the contact information below. Sent the material via email to both the company and University supervisor in cc.

Contact: Yang Soo Kloosterhof and Kerstin Bunte E-mail: yangsoo@powerchainger.nl and k.bunte@rug.nl

Related literature

https://doi.org/10.1145/2602044.2602051 https://doi.org/10.1007/s12053-014-9306-2 https://doi.org/10.48550/arXiv.1610.01191 https://doi.org/10.1007/978-3-319-61578-3_12 https://bisite.usal.es/archivos/non_intrusive_load_monitoring_nilm.pdf https://doi.org/10.1002/widm.1265 https://doi.org/10.1016/j.ifacol.2015.12.414 https://doi.org/10.3390/s121216838

General information: The "Next Move in Movement Disorders" (NEMO) project, see NEMO - Scientific research focuses on computer aided diagnosis of hyperkinetic movement disorders, which are characterized by an excess of involuntary movements, including tremor, myoclonus, dystonia, tics, chorea, spasticity and ataxia. Each movement disorder has its own clinical presentation, but frequently complex and variable mixed forms occur. Research has demonstrated that it is difficult for neurologists to distinguish these disorders if they do not see these patients frequently, and also that doctors do not always agree amongst themselves. Since hyperkinetic movement disorders have no clear anatomical abnormalities, pathology is most likely attributed to altered function of brain networks. However, so far, imaging studies have shown inconsistent distinctions in the topography of regional cerebral metabolism. This is likely due to the use of different methodologies in different groups of patients and inconsistent phenotyping. The NEMO project aims to improve patient diagnosis using computer aided methodology. In this project many different data acquisition includes multiple modalities such as video analysis, movement registration (accelerometry and EMG) and neuroimaging including functional magnetic resonance imaging (fMRI). Data acquisition for the NEMO project is ongoing and, currently, we have for instance EMG data from over 170 participants.

A1) Automated detection of myoclonus bursts Supervision: Elina van den Brandhof and Michael Biehl Level: BSc thesis, internship or MSc thesis project Suitable for 1 or 2 students of CS (or possibly AI) Research questions/tasks:

     o   Review: Which machine learning algorithms could be useful to automatically detect myoclonus bursts? Based on application in, for instance, EEG and seismological data.
     o   Application: Can myoclonic bursts be identified automatically in 
                  EMG and/or accelerometry data using the machine learning algorithms   
                  found in the review?  Implementation and testing of suitable methods.
              Available data: myoclonus burst labeled data from (NEMO and KNF data) 

The goal of the student project(s) is to automatically detect myoclonus bursts using machine learning. During the clinical diagnostic process, these bursts are often labeled by hand if clinical neurophysiology measurements are performed. This is done to quantify burst frequency and duration, which facilitates the subclassification of myoclonus and its neural origin (cortical or subcortical). This project will start with a review of machine learning algorithms (for instance in EEG and seismological data) suitable for burst detection in other fields, after which the suitable algorithms will be applied or developed to perform automatic burst detection and machine- learning assisted burst detection.

      A2)  Classification or clustering of myoclonus bursts
       Supervision: Elina van den Brandhof and Michael Biehl 
       Level: BSc thesis, internship or MSc thesis project 
       Suitable for 1 or 2 students of CS (or possibly AI)        

        Research questions / tasks:
               o   Review: Which supervised or unsupervised machine learning 
               algorithms could be useful to cluster or classify  myoclonus bursts?         
               o   Application: Can myoclonic bursts be classified (or clustered)     
               automatically in EMG data.
               Implementation and testing of suitable machine learning based methods.                      
               Available data: emg data, labeled myoclonic bursts           

             Here the goal is to perform clustering or to train classifiers which 
             discriminate between subtypes  of myoclonus and its neural origin 
              (cortical or subcortical), based on identified and labeled  
              myoclonus bursts (see project idea A1).

A3) Classification or clustering movement disorders based on time labeled data

       Supervision: Elina van den Brandhof and Michael Biehl 
       Level: BSc thesis, internship or MSc thesis project 
       Suitable for 1 or 2 students of CS (or possibly AI)       

Research questions / tasks:

               o   Review: Which supervised or unsupervised machine learning algorithms 
               could be useful to cluster or classify movement disorders?         
               o   Application: Can movement disorders be classified (or clustered) 
               automatically in features extracted from EMG data? 
              Implementation and testing of suitable machine learning based methods.                      
             Available data: EMG and ACC data, labeled time windows (two seconds each)           

This project focuses on EMG and accelerometry data. These data are acquired using a number of sensors placed on different body muscles. Using feature engineering and machine learning we try to find the relevant information that distinguishes one movement disorder from the other.

 For 11 patients, 20 tasks have been labeled in two-second time windows. In this project we aim to understand if disorder classification is possible per time window by using class labels per time point (i.e., the disorder visible vs not visible) and whether such models are more powerful than models trained on a single label per patient.

General information: In many real world data sets, subtle biases can obscure the information contained and mislead machine learning algorithms and their interpretation. For instance, in medical diagnosis problems, data acquired from different sources (different medical centers, scanners or processing pipelines ) can display specific properties which overlay the disease-relevant information. Similar problems occur in the presence of patient sub-cohorts (e.g. male and female) whose specific properties overlay the target information. For simplicity, we refer to these as “center effects” here but of course other biases could be addressed in a similar way. Although centers may use the same or very similar technical equipment and supposedly identical processing pipelines, very often the actual source of a sample can be identified easily, e.g. by a suitably trained classifier. In the projects suggested below, the aim is to modify the training of a classifier in such a way that irrelevant center-effects are eliminated as much as possible.

Exploration of a modified cost function for training a classifier In this project the goal is to implement a method for the elimination of center effects which is based on an available control data set which can be assumed to display only center-specific differences. In a medical diagnosis problem, this could be a cohort of healthy controls (HC) which should have identical properties across centers. The correction is applied when training a classifier for diagnosis of different diseases. It is based on a penalty term added to the loss function of Generalized Matrix Relevance Learning (GMLVQ), which measures how much the HC samples from different centers separate in the feature subspace that is relevant for the actual diagnosis.

 Supervision: Sofie Lövdal and Michael Biehl 
 Level: BSc thesis or research internship, suitable for 1 student of CS 
     (or possibly AI)
 Concrete task: 
 Implementation of the center correction (modification of existing matlab 

or python code) and testing in terms of toy data sets. Comparison with a previously developed alternative method. A possible extension could be the application to real world neuroimaging data

Supervision: Sofie Lövdal, Michael Biehl

Neuroimaging with FDG-PET can be used to diagnose various neurodegenerative diseases, as areas with lower metabolic uptake will form different disease patterns in different neurodegenerative diseases. This project aims to evaluate the performance of a classification system distinguishing between diagnoses on the spectrum of neurodegenerative diseases using a region of interest (ROI)-based approach. Here, we would like to extract features from specific regions in preprocessed FDG-PET scans, and evaluate how various methods of feature extraction and feature selection impact the performance of a classifier. This project is suitable for a BSc thesis or a MSc research internship.

Supervision: Sofie Lövdal, Michael Biehl

Neuroimaging with FDG-PET can be used to diagnose various neurodegenerative diseases, as areas with lower metabolic uptake will form different disease patterns in different neurodegenerative diseases. Nuclear imaging with PET is, however, a noisy imaging modality and factors such as age, gender, scanner, reconstruction protocol and amount of injected radiotracer all affect the signal. A common preprocessing method for FDG-PET is the Scaled Subprofile Model (SSM), where the brain scan is masked and log-transformed, followed by subtraction of group-level mean and subject mean. A drawback of SSM is that the preprocessed image only shows metabolism relative to the subject itself, so it is not possible to know whether an area with relatively high metabolism represents normal metabolism, or (abnormal) hypermetabolism. We would like to develop an algorithm that preprocesses the input image to be more true to the absolute value of the metabolic uptake rather than a relative estimate. In turn, this could potentially increase the performance of various machine learning tasks, such as classification of diseases on the neurodegenerative spectrum. This project is suitable for a MSc research internship or a MSc thesis.

The new WEAVE astronomical instrument (Dalton et al, 2018) is an imaging spectrometer, capturing data cubes of quite low spatial resolution, but very high spectral resolution. Rather than just having red, green and blue data per pixel, each pixel contains two spectra of about 4000 spectral channels each. This allows the study of astronomical objects such as interacting galaxies in unprecedented spectral detail. Motions of ionized gas within the structures can be mapped clearly, and compositions of stellar populations can be estimated. One problem in these data is the presence of cosmic ray events, which show up as bright spikes in the data cubes. The aim of this project is to detect these events, and remove the resulting spikes from the data. The basic tool that will be used is MTObjects (Teeninga et al, 2016, Haigh et al, 2021), which is a powerful source detector for optical data.

References: Dalton, G., Trager, S., Abrams, D. C., Bonifacio, P., Aguerri, J. A. L., Vallenari, A., Middleton, K., Benn, C., Dee, K., Sayède, F., Lewis, I., Pragt, J., Picó, S., Walton, N., Rey, J., Allende, C., Lhomé, É., Terrett, D., Brock, M., ... Jin, S. (2018). Construction progress of WEAVE: The next generation wide-field spectroscopy facility for the William Herschel Telescope. In C. J. Evans, L. Simard, & H. Takami (Eds.), Proceedings Volume 10702, Ground-based and Airborne Instrumentation for Astronomy VII; 107021B (Vol. 10702). [107021B] SPIE.Digital Library. https://doi.org/10.1117/12.2312031 Haigh, C., Chamba, N., Venhola, A., Peletier, R., Doorenbos, L., & Wilkinson, M. H. F. (2021). Optimising and comparing source-extraction tools using objective segmentation quality criteria. Astronomy & astrophysics, 645(January 2021 ), [A107]. https://doi.org/10.1051/0004-6361/201936561 Teeninga, P., Moschini, U., Trager, S. C., & Wilkinson, M. H. F. (2016). Statistical attribute filtering to detect faint extended astronomical sources. Mathematical Morphology - Theory and Applications, 1(1), 100–115. https://doi.org/10.1515/mathm-2016-0006

In microbiology, tracking dividing yeast cells in time series imaging is a tedious task, and some results in automatic this task have been obtained using deep learning. However, it is difficult to get sufficient ground truth data for training, and the method does not yield good results on complicated cell shapes. The aim of this project is to explore classical morphological image processing tools, to circumvent these problems. It is also possible to combine these morphological methods with deep neural networks.

References: TO DO

Nuwa Pen is the world's first smart ball point pen. Nuwa pen has successfully captured the essence of digital writing without making any compromises to the true writing experience of a ballpoint pen on any piece of paper. Nuwa Pen works with cutting edge processing power along with a suite of sensors which helps the pen figure out what the user is writing and where the user is writing. Nuwa Pen is developed by Nuwa Pen B.V. Nuwa Pen B.V. is a start-up based out of Groningen, The Netherlands. The aim of Nuwa Pen B.V is to combine the analogue world with the digital world with the motto of Making the World your canvas. At Nuwa Pen B.V. we want to revolutionize how humans write and interact with the digital world.

In this research project, you will be working on designing a binarization algorithm in C++ for low-resolution, gray-level multichannel video sequence. The binarization has to use adaptive threshold and be consistent over different channels and timestamps. The algorithm has to be highly optimized, and has to be written from scratch without using any 3rd party library, based on the rudimentary binarization algorithm implemented in our proprietary codebase. You will be working as a member of our software engineering team who you can share your idea, get support, and collaborate using Github.

Nuwa Pen is the world's first smart ball point pen. Nuwa pen has successfully captured the essence of digital writing without making any compromises to the true writing experience of a ball point pen on any piece of paper. Nuwa Pen works with cutting edge processing power along with a suit of sensors which helps the pen figure out what the user is writing and where the user is writing. Nuwa Pen is developed by Nuwa Pen B.V. Nuwa Pen B.V. is a start-up based out of Groningen, The Netherlands. The aim of Nuwa Pen B.V is to combine the analogue world with the digital world with the moto of Making the World your canvas. At Nuwa Pen B.V. we want to revolutionize how humans write and interact with the digital world.

In this research project, you will be working on designing skeletonization algorithm in C++ for low-resolution images. The image skeleton has to have sub-pixel accuracy due to low image resolution, and has to be robust to image noise. The algorithm has to be highly optimized, and has to be written from scratch without using any 3rd party library, based on the rudimentary skeletonization algorithm implemented in our proprietary codebase. You will be working as a member of our software engineering team who you can share your idea, get support, and collaborate using Github.

The common practice for machine learning applications is to feed as much data as the model can take. This is because in most machine learning applications feeding more data enables the model to predict better. However, few-shot learning aims to build accurate machine learning models with less training data. The metric-learning based few-shot image classification focuses on learning a transferable feature embedding network by estimating the similarities between query images and support classes from very few images.

In this project, we aim at improving the current few-short learning approaches for image classification. For more information, please contact: j.guo@rug.nl.

References: [1] Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo. Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning. https://arxiv.org/abs/1903.12290

The common practice for machine learning applications is to feed as much data as the model can take. This is because in most machine learning applications feeding more data enables the model to predict better. However, few-shot learning aims to build accurate machine learning models with less training data. In this project, the aim is to classify short videos under the few-short learning setting. The student will investigate different existing methods to extract video representations/embeddings, such as ConvLSTM, ViViT, 3D Resnet 50 etc.

For more information, please contact: j.guo@rug.nl.

References:

[1] Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, Juan Carlos Niebles. Few-Shot Video Classification via Temporal Alignment.

[2] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun Woo. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. https://arxiv.org/abs/1506.04214

[3] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lu?i?, Cordelia Schmid. ViViT: A Video Vision Transformer. https://arxiv.org/pdf/2103.15691v2.pdf