Testimonials
I think many teachers are very open and easy to approach
- Previous education
- Bachelor's Artificial Intelligence
- Programme
- Data Science and AI
What do you like about the specialisation and why?
Coming from a Bachelor's in Artificial Intelligence, this Master's programme is very applied, which is what I had hoped for. The programme has a lot of projects, which often times gives you some freedom to choose a direction and a challenge level that suits you (assuming your team agrees). This same freedom comes with challenges. You receive much less guidance than in the bachelor and often times there's no 'right answer' to be found in the slides, which means you need to get used to taking the initiative yourself for many courses.
What do you think about the atmosphere in class?
In general, I think many teachers are very open and easy to approach. They feel more like equals with more experience than superiors, which is wonderful and one of my favourite things about Radboud University. I feel like the students themselves are on average also much more motivated than in the Bachelor's, which is an absolute blessing considering the amount of group work.
What do you find most challenging in your Master’s specialisation?
Many courses require you to 'teach yourself' to some degree, which is something I wasn't used to. The workload for some courses is also very high, and the prerequisite knowledge listed for several courses is sometimes outdated. This led me to take some courses that I would otherwise have taken later in the curriculum, after other courses, or even not at all.
Are you currently doing an internship? Or what is your thesis about?
Neither. I am in my first year, and I'm still not sure what direction I want to go in. Could be language-related, could be nature-related, could be ethics-related, could be something else. I have diverse interests.
What are your plans once have received your Master's degree?
I honestly have no idea what exactly I want to do with it. I want to solve real-world problems, but I'm not sure which. That's actually one of the reasons I chose this Master's: it's very widely applicable in many domains. Gaining a deeper understanding of, or solving a problem with data is something highly valuable everywhere where they have data. Which, again, is everywhere.
I want to show that AI and machine learning exist not only in the digital world; they can have tangible impact on our society.
- Programme
- Computing Science
"I want to show that AI and machine learning exist not only in the digital world; they can have tangible impact on our society," says Yuliya Shapovalova, a researcher in Data Science. In this video, she explains how she uses her algorithms to solve a wide range of issues, from fundamental science to urgent societal challenges such as the energy transition in the Netherlands. She plays a role in the long-term strategic partnership between Radboud University and Alliander.
Additionally, she contributes to the MKB Data Lab, where together with students, she assists local SMEs in finding machine learning solutions for their problems.
At Radboud, we work closely together with medical centers and companies to make sure our research has broad societal impact.
- Programme
- Data Science and AI
The app gives patients and their physicians better insight in their disease progress, which enables them to create a better and fitting treatment plan. Watch the video for the full interview.
Preventing power grid congestion is like solving a super Sudoku
- Programme
- Data Science and AI
Without energy, the Netherlands comes to a standstill. Companies like Alliander develop and manage energy networks. Households and businesses receive electricity and gas through their cables and pipes. Alliander manages this for more than three million customers.
'Due to the energy transition, digitalisation, housing construction and economic growth, the electricity grid is becoming increasingly congested. In some places and times, too congested', explains Jacco Heres, data scientist at Alliander.
'We are working hard to expand the power grid, but in more and more places, the demand for electricity is growing faster than we can build', continues Jacco Heres. 'In those locations, we cannot provide additional capacity to industry, offices and supermarkets until the grid is expanded. Besides expansion, we are working on smarter and more efficient use of the power grid to prevent congestion.'
Project STORM
To prevent this congestion, better insight into all flows across the network is needed. This is where STORM helps, a collaborative project led by Roel Bouman, in which Alliander and the Department of Computing Science at Radboud University participate. Within the project team, Roel Bouman manages the project and provides technical oversight.
He graduated in Chemistry and Computing Science, specialising in data science and machine learning. 'To accelerate the project, we organised a hackathon on campus in November 2021', says Roel Bouman. 'The goal was for participants to generate ideas about using algorithms to automatically predict when switching events occur in the data.'
Automatic Filtering
'Of course, Alliander also tries to predict when and where congestion will occur through measurements. But measurement data always contains errors and irregularities. Moreover, "switching" takes place - alternative routes when a cable breaks somewhere, for example. This makes other routes busier.
If you want to measure the actual load and make predictions for optimal use, you need to filter out incorrect data. Preferably automatically', explains Roel Bouman, 'because if people do it, it takes a lot of time and you depend on their expertise and availability.'
Mathematical Model
'With machine learning, you are always operational and have current data at any moment.' Roel Bouman's team developed a mathematical model suitable for analysing data using algorithms. 'It's like solving a puzzle, a kind of super sudoku. Fifty percent doing and fifty percent thinking. We created a demonstrator where the data analysis happens entirely digitally. It works well and is now being implemented at Alliander by Jacco and his team.'
Is this THE solution? 'Yes', says Roel Bouman. 'Although updates will always be needed. Every system requires maintenance.'
We need to make AI more interactive so that it better aligns with how our brains work.
- Programme
- Data Science and AI
After conceiving and designing holographic video conferencing, interactive drone swarms (flying Lego) and the foldable display, Roel Vertegaal (56) is now working on making artificial intelligence (AI) more interactive. Language generator ChatGPT is only the beginning of various AI applications, Vertegaal observes. 'Artificial intelligence that makes quick decisions and is completely private and secure doesn't exist yet. That's what we're researching.'
Human Media Lab
Vertegaal has been a professor at Radboud University Nijmegen's Faculty of Science since May 2024. He holds the Chair in Human-Computer Interaction. In this field, he previously worked as Director of Research at Huawei Consumer Business Group, where he conducted research on human-computer interaction, and as a professor at Queen's University in Ontario, Canada. There, Vertegaal founded the Human Media Lab, which has been based in Nijmegen since spring 2024.
This leading lab focuses particularly on research into interactive systems inspired by the structure and functions of our brain. 'This allows us to make AI more interactive and better aligned with how our brains work,' Vertegaal explains. 'It's about systems that help you perform tasks effectively.'
Curious Child
Vertegaal was born in Hazerswoude near Leiden and was a curious child. 'As a seven-year-old boy, I invented a kind of remote control for the TV,' he recalls with a smile. 'I used a long metal rod that allowed me to operate the conductive buttons on the TV from the sofa. A bit like today's touch screens.' At pre-university level, he was interested in science subjects, but also in languages, geography and economics. 'I'm not purely science-oriented, but I was a quick learner and easily got bored at school.'
Vertegaal decided to study music at the conservatory in Utrecht. 'They already had an Apple Macintosh computer at that time. As a 17-year-old, I was completely fascinated by it, that's how I started programming. In Utrecht, I also became familiar with AI and algorithms that can generate music.'
The next step was Vertegaal studying computing science at the University of Bradford in England. He earned his PhD in Cognitive Ergonomics from the University of Twente and became Professor of Human-Computer Interaction at Queen's University in Ontario, Canada.
New Interaction Techniques
New interaction techniques are necessary because new technologies often don't sufficiently consider human interaction and how our brains function, Vertegaal believes. For example, he graduated from the University of Twente in 1998 with such a new interaction technique.
'During video conferencing, I noticed that much confusion arose because it wasn't clear who was speaking and to whom someone was talking. This happens because eye contact isn't possible through the screen, causing this non-verbal information to be lost. Using eye trackers, which people with disabilities use to control their computers, I developed software that enables eye contact during video conferencing.'
Foldable Phone
Other Vertegaal inventions include attention detection in the iPhone and the foldable phone launched by Huawei. This phone consists of three foldable sections that together form one display equipped with an invisible grid of touch sensors.
If it's up to the professor, three-dimensional forms will return, for example in flexible – read: bendable – displays. 'Flat screens are limited. In the normal world, you also have forms. The foldable phone is a first step in that direction.'
Science Ahead of Practice
Vertegaal designed the first prototype of the foldable phone, the PaperFold, back in 2014 with student Antonio Gomes. The Huawei Mate XT is based on this prototype and won't be launched until September 2024. 'It always takes about ten to twenty years before scientific discoveries become market-ready products,' Vertegaal notes. 'That's how far science is ahead of practice.'
Conceiving a triple-fold screen for a phone is one thing, but many more steps follow. 'The first trifold now on the market features a very thin, fully foldable and flexible OLED screen,' Vertegaal explains. 'Huawei developed a very special hinge mechanism for this, allowing you to fold the screen flat without creasing or damaging it.'
'Flying Lego'
About seven years ago, with researchers and students from the Human Media Lab at Queen's University and the Lego Creative Play Lab, Vertegaal developed 'flying lego'. The lego bricks can fly independently, like an interactive swarm of coloured mini-drones. By equipping the mini-drones with markers read by a 3D camera, the mini-drones can fly as a group of pixels.
Vertegaal: 'With this 'next-level-lego', we've shown that it's possible to create displays consisting of physical pixels that you can touch and simply sculpt in the air.'
Disappearance of Human Interaction
The digitalisation of modern society has led to the disappearance of various forms of physical human interaction, Vertegaal observes. He wonders aloud whether the increasing social deterioration in the world is related to this.
'When you type messages instead of communicating physically, person to person, you don't see each other and can't hear each other's voices. Facial expressions and tone of voice provide much information. As humans, we've evolved into beings that derive quite a lot of information from non-verbal communication. You lose that with much digital technology. That's why we simply need to develop better, interactive technologies.'
Anti-technologies
New technologies are called anti-technologies because they try to undo problems caused by earlier technologies, Vertegaal explains. The professor cites phone addiction as an example of an unwanted technology effect.
'The attention-controlled phone that I designed is a response to that. With technology that registers whether you're looking at the phone, you can, for example, reduce your notifications to make them less intrusive. Apple has already applied this to its phones.'
Vertegaal supervises students in the Human Media Lab and conducts research with them into new technologies. 'As a scientist, your goal is to develop knowledge through research funded by public money and industry that makes the world a better place.'
This article was previously published on Techgelderland.nl. Photo credits: Linda Verweij.
Working alongside such engaged classmates makes even challenging group projects rewarding.
- Previous education
- BSc Artificial Intelligence (Radboud University)
- Programme
- Data Science and AI
What do you like about the programme/specialisation and why? How has the programme/specialisation challenged you?
My transition from a Bachelor's in Artificial Intelligence to this Master's programme has been transformative. While my undergraduate studies laid theoretical foundations, the Master's programme's hands-on & project-based approach has pushed me to think differently. I now have the freedom to dive deep into areas that fascinate me, but this comes with greater responsibility. The programme demands significant independence and initiative. Moving from theories to hands-on problem solving has redefined both my approach to learning and my career trajectory.
What do you think about the atmosphere in class?
The programme's collaborative atmosphere sets it apart. Rather than maintaining a rigid hierarchy, teaching staff foster an open environment where students feel comfortable seeking help and sharing ideas. This approachability extends to my peers, who bring genuine enthusiasm and diverse perspectives to every discussion. Working alongside such engaged classmates makes even challenging group projects rewarding.
What do you find most challenging in your Master’s (specialisation)? Have you encountered any obstacles?
While the programme's high degree of independence initially presented a challenge, I've come to value this aspect of my Master's experience. The freedom to shape my curriculum through electives is both liberating and demanding. It requires careful planning to manage course workloads and self-directed learning (skills I had to develop along the way). Choosing from the big number of elective options can feel overwhelming at times, but I greatly prefer this flexibility to a rigid curriculum. The autonomy has pushed me to become more intentional about my academic choices and learning journey.
Are you currently doing an internship? Or what is your thesis about?
I am currently in discussions with some companies about potential internship positions, particularly focusing on roles involving computer vision and deep learning.
Why do you think it is important that there are people with this degree? What are your plans once you have received your Master's degree?
In today's data-driven world, Data Science has become the cornerstone of innovation across virtually every industry. By transforming complex data into actionable insights, Data Scientists unlock solutions that automate routine tasks and empower people to focus on creative work. This expertise is crucial as organisations increasingly rely on data-driven decision making to stay competitive and drive innovation. After completing my Master's degree, I aim to work in an environment where I can apply my analytical skills to solve challenging real-world problems, turning complex data into practical solutions that make a meaningful impact. The field's rapid evolution and growing impact across sectors make it an exciting time to contribute to this transformation.
My research uses simulation models and AI to better understand biological processes, particularly cell movement.
- Nationality
- Nederlands
- Programme
- Data Science and AI
Could you introduce yourself?
I studied at Radboud University (BSc in Molecular Life Sciences and Chemistry, MSc in Molecular Mechanisms of Disease). Although trained in life sciences, I've worked in the Data Science department since 2021. I believe we need a better understanding of data to separate signal from noise in biological data. My research uses simulation models and AI to better understand biological processes, particularly cell movement. Because my field is interdisciplinary, I teach across various programmes. I give lectures on basic mathematical models in a Bachelor's in Biomedical Sciences minor, and Master's students in Data Science and Artificial Intelligence know me from the Natural Computing course. There, we examine how simulating biological processes helps us better understand biology - and how this leads to algorithms applicable to broader problems, like evolutionary algorithms and swarm intelligence.
Why did you choose to study/work in this field? What makes it so interesting?
I've always found life sciences fascinating. During my studies, I observed that while technological developments provide increasingly complex data, extracting meaningful insights can be challenging. Contrary to the saying "measuring is knowing", data collection is just the first step towards new knowledge. Anyone researching human biology encounters this: crucial processes like cancer growth and immunity are incredibly complex. While technological advances allow us to measure more, this doesn't automatically translate to understanding how these processes work. That's why I work with simulation models: they help us systematically test our knowledge, better understand what data to expect, and make our research more robust.
What is your current research focus?
I'm currently focused on cell movement. Special microscopes can capture moving cells, but analysing the resulting videos is often challenging. In my current research, I combine simulations with AI to assist with this. For example, I investigate how cell movement simulations can help develop better AI tools for video analysis, and how AI can improve cell movement simulations. Want to learn more about my research? See this page about the above project, our Computational Immunology group website, this blog post about an earlier project, and this page if you'd like to know more about simulations.
What immediately grabbed me in this field is the combination of theoretical puzzle-solving and practical application.
- Nationality
- Nederlands
- Programme
- Data Science and AI
Can you introduce yourself?
My name is Tom Heskes, and as a teacher and researcher within the Computing Science and Artificial Intelligence programmes, I help students find their way in the world of AI. Although as vice-dean of research I have less time to teach, I still love exploring new ideas about AI together with students. With years of experience in artificial intelligence, I have seen many trends and hypes come and go. This gives me good insight into what is truly groundbreaking and what is mainly marketing talk.
AI is a field full of exciting developments, and I enjoy helping students understand and contribute to them. Besides my work at the university, I have been involved with various AI spin-offs and am committed to increasing AI literacy, even outside the university. Perhaps our paths will cross in a lecture, project, or discussion about the future of AI!
Why did you choose to study/work in this field? What makes this field so interesting?
What immediately grabbed me in this field is the combination of theoretical puzzle-solving and practical application. Developing new AI methods – a mix of mathematics, algorithms, and creative programming – and then being able to immediately apply them to solve real problems remains amazing every time. Whether it's medical diagnostics, sustainability, or smart technology, AI offers endless possibilities to make an impact. This combination of intellectual work and direct application is what makes this field so interesting to me.
What are you currently researching?
My research focuses on machine learning: developing AI methods that learn from data. I work on both the theoretical side – developing new methods and better understanding them – and their practical application. The latter is often done in collaboration with experts from other fields. For example, we work with clinicians on better treatment methods for Parkinson's disease, help grid operators like Alliander make the electricity grid more robust, and support other scientists with complex issues, such as automatically generating mathematical proofs or detecting gravitational waves. AI opens doors to surprising applications, and that's what makes this research so exciting.
What tip do you have for students who are choosing their field of study?
Choose something you enjoy and through which you can make an impact. The best scenario is when you study with pleasure while contributing to the world around you.
What do you enjoy most about working with students?
The best part is when students suddenly understand how AI works and why it's so powerful. That moment when theory and practice come together and they become enthusiastic about developing new things themselves. Moreover, their questions and fresh ideas help me stay current in this rapidly developing field.
Since 'AI' is the buzzword nowadays, being able to understand what goes behind the hood is really necessary.
- Previous education
- Bachelor of Technology, Computer Science (Manipal University Jaipur, India)
- Nationality
- Indian
- Programme
- Data Science and AI
- Study start date
- Study end date
What do you like about the programme/specialisation and why? How has the programme/specialisation challenged you?
What I love the most about my Master's in Data Science is the flexibility in choice of courses I received. I have always been interested in Speech and Language technology, and I was really excited to start the programme when I learned that we could choose courses that fit our interests from other faculties as well. It was of course quite different from how courses were taught in my home country. Here, I got to do more research work and gain in-depth understanding of the concepts.
What do you think about the atmosphere in class?
The atmosphere in the classroom has always been quite relaxed. The teachers have been really friendly and supportive. They were easy to reach out to! Further, the teachers made sure that we could always reach out to them outside of the classroom, using discord or email.
What do you find most challenging in your Master’s (specialisation)? Have you encountered any obstacles?
It took me some time to get used to the new grading system here. At times, I would be confused about how my exam paper has been graded but soon enough, I got used to the system and everything worked out in the end.
Are you currently doing an internship? Or what is your thesis about?
Yes, I just started my thesis with a researcher at CWI (Centrum Wiskunde & Informatica). It's about Bias in Book Recommender Systems. I am working on understanding how books of specific themes/ bias are recommended more than others.
Why do you think it is important that there are people with this degree? What are your plans once you have received your Master's degree?
I think a degree in Data Science in today's age can equip you with necessary skills needed to build technology reduces human effort and enhances quality of life. Particularly, since 'AI' is the buzzword nowadays, being able to understand what goes behind the hood is really necessary.
Once I graduate, I plan to work in an AI-based company and put the skills I learned here to use. However, since I am an advocate for Responsible and Ethical AI, some day I can see myself doing something in this field on my own.
Data is everywhere. Being able to handle it well is one of the most useful skills to learn.
- Previous education
- BSc Computing Science (Radboud Universiteit)
- Programme
- Data Science and AI
- Study start date
- Study end date
What do you like about the programme/specialisation and why?
There is a wide range of topics where you can apply Data Science and this Master's explores many of them. You can choose whether you want to learn more about speech recognition, legislation or prosthetics. The 6 EC completely free choice are a nice bonus.
What do you think about the atmosphere in class?
Particularly good. I have regularly worked with strangers and I am always positively surprised. Everyone communicates clearly, does their job and we have a pretty good time.
The lecturers are very polite, attentive and there is a friendly atmosphere. I have gotten into the habit of addressing teachers by their first names, as they usually prefer that.
What do you find most challenging in your Master’s (specialisation)? Have you encountered any obstacles?
As in the Bachelor's, computers are sometimes just really irritating to work with. I find Machine Learning models in particular a perpetual source of frustration. Often you make a small change and then have to wait for hours to see if anything improves. Not my thing.
Are you currently doing an internship? Or what is your thesis about?
I did a six-month internship at KNMI on AI models that do weather forecasts from 2 to 8 weeks. An interesting topic, I think. Yet I got completely bogged down in the project because, despite many efforts, I couldn't get some of the models to work. Very frustrating. I quit and am now doing an internship on a completely different subject, improving a search engine. Pity about the study delay, but I would definitely recommend doing something that doesn't make you unhappy.
Why do you think it is important that there are people with this degree? What are your plans once you have received your Master's degree?
Data is everywhere. Being able to handle it well is one of the most useful skills to learn. After finishing the Master's, I will look for a company with a nice team to work in.
I find it extremely exciting to dive deeper into the biomedical world and to figure out which important problems exist where we as computer scientists can really make a difference.
- Nationality
- German
- Programme
- Data Science and AI
Could you introduce yourself?
My name is Johannes Textor, and I came to Nijmegen almost 10 years ago now, where I initially started setting up my own research group at Radboud university medical center (Radboudumc). I began my career in theoretical computer science and artificial intelligence (in a time when this wasn't yet "trendy") and gradually became more interested in using computational methods in biomedical research, particularly in immunology. But after several years, I began to miss fundamental research a bit and therefore seized the opportunity in 2020 to partially move to Radboud University. Currently, my research group is present on both sides of the "Heyendaalseweg" (the road in between the Faculty of Science and Radboudumc, ed.). As a student, you might encounter me in the Bachelor's and Master's Computing Science programmes, particularly Data Science courses, but also in the Faculty of Medical Sciences in courses such as "Bioinformatics" and even "Excellence in Communication". I am also a member of the education directorate as coordinator of the Master's in Computing Science.
Why did you choose to study/work in this field? What makes this field so interesting?
I have already experienced multiple "hype cycles" in which it was repeatedly promised that we would solve major problems with artificial intelligence, such as curing cancer or finding a vaccine against HIV. These promises haven't really materialised, but there has definitely been progress in the biomedical sciences thanks to computational methods - think of the development of mRNA vaccines, where "in silico" predictions have long played a crucial role. I find it extremely exciting to dive deeper into the biomedical world and to figure out which important problems exist where we as computer scientists can really make a difference.
What is your current research focus?
I am very interested in how the immune system processes information, and how and why this sometimes goes wrong (think of autoimmune diseases). For this, I build large simulation models that help us better understand the functioning of T cells and other cells of the immune system (for more information, see a seminar of mine on this topic here). I also develop AI methods to analyse immunological datasets more quickly and objectively.
What advice do you have for students making their study choice?
What do you really want to do later? What do you find important and what are you good at? What makes you happy? Think about it and talk about it with your friends and family.
What do you enjoy most about working with students?
I see it as a privilege to guide young people through a part of their development. Moments when you can clearly see that a student is making progress and gaining satisfaction from it give a lot of energy.