ICAI Interview with Jeanne Kroeger: Making ICAI a household name

As project manager of ICAI Amsterdam, Jeanne Kroeger deals with the business and organizational side of the labs, occasionally receives delegates from abroad to talk about ICAI, and is now busy organizing the first physical social meetup on June 30th for the Amsterdam location. Kroeger: ‘It is important to create environments where people can meet their colleagues in an informal setting. I hope that all ICAI cities can join this social event.’

Jeanne Kroeger

Jeanne Kroeger is project manager of ICAI Amsterdam and before that she was community manager of Amsterdam Data Science. Kroeger has a Master’s degree in Chemistry from the University of Liverpool.

What is the idea behind the ICAI National Social Meetup on June 30?

‘The purpose of this social event is to have one moment where ICAI members across the whole country can come together at their location to meet their colleagues in an informal and relaxed way. The idea is that other ICAI cities will join in and that they will host their own physical meetup for all ICAI members involved in that city. Amsterdam and Nijmegen will host their own events. There will be a broadcast at the same time with a five-minute connection on screen with a few words from Maarten de Rijke, director of ICAI. Other than that, it is an informal gathering. It’s really an opportunity for everyone to meet and chat. It is accessible to all ICAI members, from junior, senior and support staff, and within academia, industry, non-profit and government. We will host the meetup from three to five pm, so it’s within working hours.’

Why is ICAI organizing social events like this?

‘I think there is a lack of community feeling in every organization right now. Because of covid, all the people who started in the last two and a half years have not had the opportunity to come into the office. In Amsterdam, for some people this event will be the first time they meet other ICAI members in person. All the labs focus on specific things, but there’s transferable knowledge across the labs. In my previous role for Amsterdam Data Science, I could see that some people were working on very similar topics, but had no idea about each other. It is important to create environments where people feel like they can come and meet their colleagues in an informal setting. The environment in which you work is so crucial. For me it’s almost more crucial than the content because it’s what gives me the energy and motivation to continue.’

How well do the people from the different ICAI Amsterdam labs know each other?

‘I recently had organized lunch for the ICAI Amsterdam lab managers. There were ten of us in the room and only two people really knew each other. The rest had never spoken to each other, while some of them have their offices maybe five doors down from each other. So there’s something to be said about creating more of a community in ICAI Amsterdam and the other hubs and then across those hubs.’

What should the ICAI community look like in four years?

‘I think ICAI should be a household name. The general knowledge about ICAI is starting to build. The ICAI labs have been producing incredible results in the last five years and have made incredible collaborations. We are forming a solid network of labs and the aim is to build more connections across the country. I’ve had meetings with delegates from other countries to talk about ICAI. The word is going out about ICAI!’

Which organizations from abroad visited you to talk about ICAI?

‘We had a delegation from Estonia and I’ve had conversations with large international companies. I think in four years it would be great for the ICAI format to be more standardized. The Netherlands is really well-positioned: it’s a great international hub, easy to get to and it has an amazing standard of living. We are at a point where new AI initiatives are coming out, and it would be great if we can make sure that we position all of these initiatives together, so that they are acting in the same direction as opposed to competing against one another. ICAI has really put itself on the right path to make the Netherlands an important research AI hub.’

What were the main questions these delegations came with?

‘A lot of them were amazed by the amounts of money the labs received for fundamental research. Their main question was basically how the ICAI labs managed to do that. You don’t see this willingness of companies to fund fundamental research in many other countries. To get a five-year commitment from companies, that’s just phenomenal.’

What will be the main challenge for ICAI in the future?

‘ICAI has got that nimbleness. It’s very agile and flexible. Prestigious organizations like the European ELLIS, the Royal Society in the UK or the KNAW in the Netherlands have become so large that things can start to move very slowly. ICAI is growing right now, but I hope it can keep that nimbleness. I think this is possible if ICAI keeps evaluating and keeps seeing what it needs to be.’

Would you like to get to know your fellow ICAI members and have a drink with them? Sign up for the ICAI National Social Meetup – Summer Drinks on June 30th!

ICAI Trio Interview: AI entrepreneurship and a shared ownership of talent

It has been four years since ICAI kicked off and in the meantime ICAI has grown from 3 to 29 labs. How is ICAI doing so far? We take stock of the situation with a lab manager, a PhD student and the scientific director.

Efstratios (Stratis) Gavves is (former) lab manager of QUVA lab, co-director of QUVA and POP-AART ICAI labs, associate professor at University of Amsterdam and co-founder of Ellogon AI BV.

Natasha Butt is first year PhD student within QUVA lab, has a MA degree in Data Science and a BA degree in Econometrics.

Maarten de Rijke is the scientific director and co-founder of ICAI, professor of AI and Information Retrieval at the University of Amsterdam.

What was ICAI’s original purpose? Has that changed in the last four years?

Maarten: ‘The original vision was that we felt that more needed to be done to attract, train and create new opportunities for AI talent, while at the same time we wanted to work with a diverse set of stakeholders on shared research agendas. The underlying idea was that AI can make a positive contribution in lots of societal areas. We have been trying things out. And you learn by doing; that has been the mantra since day one and that will not change. One thing that is changing though, is that the first ICAI labs have matured and that there is a follow-up contract that is not just about attracting and training talent, but also about retaining talent. With the Launch Pad program we want to help the PhD students find their next opportunity in interesting places, ideally here in the Netherlands. Similarly, as PhD students begin to graduate from their lab, some of them have entrepreneurial plans. With the new Venture program we look at how we can help them connect to the right stakeholders and funding. So it’s still the same mission, but the instruments expand.’

ICAI has grown from 3 labs to 29 labs in the past four years. What is it like to work in a research lab with external partners?

Natasha: ‘What I really like is that you get to meet and collaborate with so many different researchers within industry. For a PhD student starting out this is really interesting and exciting. I can’t really weigh in on the negatives because we haven’t published a paper yet.’

Maarten: ‘Especially in labs where the non-academic partners don’t have a long tradition of research, it can be a challenge to identify good problems that matter academically and industrially. You need good problems that don’t need ten years to solve, but that also cannot be solved in three months. Aligning the horizons and expectations is something that needs attention.’

Stratis: ‘Working with external partners is inspiring and fruitful. The cornerstone for a successful relationship is managing expectations. Generally one could say that companies like stability and structure, while researchers in the university thrive with creative chaos. Finding a good balance between these two can bring great results. In fact, in my experience I have seen this work quite smoothly, because we have been lucky that the people involved are very conscious and open-minded.’

‘From now on funding will be less expected from government structure and instead come from private initiative.’

Stratis Gavves

To what extent do universities and companies or governmental organizations need each other in developing AI that can make us more future-proof?

Maarten: ‘We see a slow change right now in the ownership of big challenges. It is no longer just governmental, academic, or industrial, but much more a shared ownership. We are coming to the realization that the best way to tackle climate, health, energy and logistics problems, is to go after these problems together. All of these big challenges are multi-stakeholder and multi-disciplinary. For example, when you’re working on computer vision, at some point you will run into some legal or ethical questions that are tough. Think of all the deep fakes. On the one hand these generative models are fantastic and creative, but there’s another side. An algorithm developer should hang out every now and then with people who bring a different perspective to the table.’

Natasha, you are from Great Britain. Stratis, you are from Greece. Are there initiatives like ICAI over there?

Stratis: ‘I think ICAI is a very successful experiment that will be followed, one way or the other, by other countries. We had some preliminary conversations in Greece and I think that there is interest for sure.’

Natasha: ‘In the UK I haven’t come across many things like ICAI. But when I studied at UCL in London, there were a lot of AI societies and entrepreneurship societies that would hold events and invite students from other universities. So there’s definitely an appetite for it. Especially in London there are a lot of hubs and all the universities are pushing it.’

‘Collaborating with so many different researchers within industry is really exciting for a PhD student just starting out.’

Natasha Butt

Are there countries that were an inspiration for ICAI?

Maarten: ‘Yes, the Von Humboldt fellowships in Germany for example. And especially the attitude behind it was an inspiration for us: start with talent, bring the talent to the country, and then invest and create opportunities. We also saw the same attitude in France.’

Stratis: ‘The instrument that ICAI presents, is an innovation by itself. And this success will be broadcasted to other countries, because there is a need for it. This is how things will be working from now on: funding appears to be less expected from government structure and instead come from private initiative. People are searching for alternative sources of funding and I think that ICAI presents a fair way of doing this in such a way that both sides benefit.’

What are the plans for the next four years?

Maarten: ‘We are working on a large new program, funded by NWO, to expand ICAI with 17 new labs. I hope that by the end of this year we will have around 50 labs. Part of the plans is to expand to all academic cities. We would like to reach out and help people there to get going. Another thing is that our colleagues, whom we are heavily involved with in Nijmegen, have set up AI course programs for medical professionals. We are trying to see how we can do similar things, but then for other sectors like logistics and civil servants.’

Stratis: ‘My goal is to get Natasha and her lab mates graduate. And to attract more industries into the concept of ICAI, perhaps export it outside the Netherlands and maybe even to Greece. And of course, to keep doing top-notch research.’

‘More and more people are coming to the realization that the best way to tackle climate, health, energy and logistics problems, is to go after these problems together.’

Maarten de Rijke

Do you have questions for each other?

Natasha: ‘I would like to know what plans there are for the future. What sort of events do you hope to put on, especially from a PhD perspective?’

Maarten: ‘We want to organize as much as possible as the PhD students need. So we should listen to what would help you. The ICAI Launch Pad program helps PhD students who are towards the end of their PhD trajectory. But of course early stage PhD students have different needs, plans and questions. So we’d like to hear how we can help to make this a better experience. So far, we have put a lot of focus on sharing expertise and experiences, but of course there’s more to being an AI PhD student than that. You Natasha, and other PhD students, should be the ones that tell us.’

And where can she go with her ideas?

Maarten: ‘YaSuei Cheng, the ICAI community manager, can help organize things or find the right people to get something going. And here in Amsterdam we have quite some experience in setting up internships. But I’m sure that there are many things that we’re not seeing, so please let us know.’

Stratis: ‘I was wondering, what is the idea on how to get new spin-offs into existence? Is there guidance there? Let’s say that Natasha comes up with a great idea that her lab partner Qualcomm is not interested in. What should she do?’

Maarten: ‘We’ve teamed up with an initiative called TTT-AI. This organization is all about tech transfer and helping people finding out if there’s a market for their ideas. This initiative works around the whole country. It wants to connect the local ecosystem with local researchers, but also share systems across the country.’

The next ICAI Day on June 1st will be about AI entrepreneurship. Stratis, as co-founder of Ellogon AI, you know a thing or two about this. What is it like to launch a company from lab to the market?

Stratis: ‘I’m still learning, so I can’t tell you the full story from A to Z, but maybe from A to F. It is a lot of fun actually. We are the new generation of academics. It is expected, or at least appreciated, if we look at possibilities like this. But I’m not sure that everyone will be cut out for it. In a way we are working double jobs. It’s really rewarding though in many ways. What I found really interesting, is that so many academics and researchers already have moved to industry. And maybe there is something beyond the obvious argument that people only go there because there are better salaries. I can confirm that creating your own company, working on real problems and solving completely different issues, is really interesting.’

Natasha, how do you feel about making the move to industry in the future?

Natasha: ‘I’m pretty open-minded. It would be really nice and useful to hear the experience of people who went to industry and people who stayed in academia. Doing internships would also help.’

Maarten, what would you advise PhD students in finding the next step?

Maarten: ‘I think it’s a great idea, like Natasha says, to try out a few internships. I generally recommend to go to a completely different team and work on different problems. A different experience helps you to shape your thinking about what you’d like to do next. Maybe even consider doing an internship with a NGO. The Red Cross for example has loads of interesting challenges.’

And what can be done to help researchers to set up AI startups?

Maarten: ‘Mentoring is always useful. To hear other voices and to speak with friendly but critical colleagues who can walk alongside you for a while and connect you to potential customers and challenging problems.’

Stratis: ‘Once you’re in a company, you’re living in borrowed time until you really make it. Learning how to run a company, while developing a product, can be hard. So one thing that can be done is to familiarize people with this aspect of entrepreneurship so that they can anticipate the difficulties. And there are so many things that can be quite easily solved that can still make a huge difference.’

Would you like to meet your fellow ICAI members? On June 1st, the hybrid Summer Edition of the ICAI Day takes place. The theme of this edition is ‘AI Entrepreneurship: From the lab to the market’. Sign up!

ICAI Interview with Rianne Fijten: Tightening the relationship between medical clinics and commercial parties

In order to implement new AI technology in medical clinics in a sustainable way, close collaboration between the clinic and commercial parties is crucial, argues Rianne Fijten. ‘You need to make sure that if the grant money runs out, which it always does, the product that you built is not just lost.’

Rianne Fijten

Rianne Fijten is one of the scientific directors of Brightlands Smart Health Lab, assistant professor and senior scientist of clinical data science at Maastro clinic.

Brightlands Smart Health Lab is a collaboration between Maastricht University, Brightlands Institute for Smart Society, Zuyd University of Applied Sciences, Maastro Clinic, Maastricht UMC+, ilionx and Netherlands Comprehensive Cancer Organization.

Could you tell me about the research happening in the lab? What makes this research unique?

‘What is interesting about our lab, is that we really go from technology to the clinic. That’s a concept I’ve not seen anywhere else. Usually a research group focuses on a very specific part of a pipeline, problem or societal issue. Within the lab we have three pillars: data infrastructure, data science and clinical implementation. It is a pipeline from start to finish: we set up the infrastructures to get the data out of the hospitals, extract the data, build AI models and then implement it into the clinic.’

‘Another important thing is that we are close to business. Getting data science into a medical clinic is difficult, but getting it into the clinic without a commercial party involved, is even more difficult. To make sure that the new techniques are supported and maintained, it is crucial to connect the clinic to commercial parties, because researchers will not sustain it after their research is done. They have other research to do.’

What is your personal mission within this lab?

‘My main focus is on the last pillar. Since AI is booming business, so many AI-models have been built. But what you see in healthcare is that implementing those in the clinic is the difficult part. So we try to implement clinically relevant tools, but also find out why research doesn’t end up in the clinic, and what the problems and issues are in that process.’

What kind of clinical needs are you addressing?

‘A good example is a decision aid for prostate cancer patients that we built with the company Patient Plus. As every treatment has different side effects, this tool gives patients the option to find their personal risks of getting side effects, based on their personal characteristics. Prostate cancer is an interesting choice for a decision aid tool, because this disease has a very high survival rate, which makes it possible for patients to choose between different treatments. Patients answer questions like ‘what is your age?’, ‘do you smoke?’ or ‘are you a diabetic?’ Those are all risk factors for incontinence for example. At the end the patient will get a visualization of their personal risks and learns about the disease along the ride. For this tool we have set up a collaboration with urologists that we know very well. And we then offered it to a company, under certain conditions of course, so that they can make sure it will be used in the clinic in the future.’

The lab collaborates with seven different partners. What is it like to work with so many partners?

‘It gives us a lot of flexibility. Working with this big pool of collaborators allows us to set up different alliances that are suited to answer a specific question or solve a specific problem.’

All nine PhD students of the lab are located physically at the partners and mentored by senior scientists at the partners. Why did you choose that approach?

‘In order to keep the collaborations alive and to keep the relationships good, it is important to work together, even if you don’t have a specific project that you are working on that very moment. I think it is very important to establish long-term relationships and by working together in supervision of these PhD students you achieve that.’

What do you want to have achieved in four years?

‘If anything comes out of our ICAI lab, I hope that it is raising more awareness about closer collaboration with the clinics and industrial partners. What we see a lot within the projects is that at first the people at the clinic don’t really see the need for or are a bit anxious to involve industrial parties. I don’t know why, I think it’s the non-profit versus for-profit problem. I hope that with the projects we are going to do within the ICAI lab, that this is one of the take-home messages that we can deliver. We are currently forming the bridge, and hopefully in the future they can keep finding each other without our help.’

On April 21, 2022, the Brightlands Smart Health Lab will talk about their current work during the lunch Meetup of ‘ICAI: The Labs’ on AI for Radiation Treatment in the Netherlands. Want to join? Sign up!

ICAI interview with Evy van Weelden: Finding your way in the PhD maze

Corona has made it more difficult for PhD students to find each other, while this group benefits a lot from being part of a community. Evy van Weelden started her PhD in 2020 and only saw her fellow PhD students half a year later in person. ICAI is now organizing its first PhD social meetup. Van Weelden: ‘A PhD is like a maze in which you have to find your way. I feel like I could learn a lot from PhD candidates that are in their third or fourth year.’

Evy van Weelden

Evy van Weelden is a PhD candidate within MasterMinds Lab.

MasterMinds Lab is a collaboration between Tilburg University, Fontys Hogescholen, ROC Tilburg, Actemium, CastLab, Interpolis, Marel, MultiSIM BV, Municipality of Tilburg, Port of Rotterdam, Royal Netherlands Air Force, SpaceBuzz, TimeAware and WPG Zwijsen.

The research reported in this study is funded by the MasterMinds project, part of the RegionDeal Mid- and West-Brabant, and is co-funded by the Ministry of Economic Affairs and Municipality of Tilburg.

Working on flight simulations with the Royal Netherlands Air Force and MultiSIM sounds exciting. What amazed you so far?

‘Before I started I had some experience with virtual reality (VR), but when I first tried the flight simulation I was very impressed with how realistic it was. The company MultiSIM models PC-7 aircrafts exactly how they are in real life. Some people are prone to simulator sickness, but I’m not, so it was very fun. You feel very present in that visual environment, which is very important for the motivation of learning. There are different levels why this simulation is so realistic: the sounds, the environment and if you put pressure on the stick or throttle, the response of the aircraft is exactly how it would be in real life.’

What exactly are you researching?

‘My project focuses on neurophysiological indicators of learning in VR flight simulations. I am currently looking at the difference between desktop flight simulators and a VR flight simulators. To what extent does the fidelity of the simulation – so the degree to which the flight simulation resembles a real flight – influence the subjective workload or flight performance of the user and their brain activity? This topic fits in several types of fields, but the main one is neuro-ergonomics. With ergonomics you look at how a person interacts with a system. But neuro-ergonomics is more specific: you’re actually looking in the brain while this person interacts with a system, computer or machine. Once we have established models of the brain activity during training, we can try to predict the learning curve in VR flight simulations. Eventually we want to give neuro-feedback to the user, in the hope that it would increase their learning curve.’

What is it like to do your research with two external partners?

‘There is a lot of communication involved, with the partners, and internally with my supervisors at Tilburg University. And there is a lot of brainstorming. Everyone is enthusiastic and proactive. The meetings with the people from the partners are fun and inspiring. They are intelligent and have a lot of content-related feedback.’

What does the collaboration look like in practice? Do you go there?

‘My past data collection took place at Mindlabs, but for the next studies I plan to use the pilot trainees. That will take place in Soesterberg where the Airforce and Multisim are settled or I go to Woensdrecht in Zeeland where the pilot training takes place.’

You started with a study in neuroscience. How did you get into AI?

‘During my masters I did an internship that considered brain-computer interfaces (BCIs), and after that I knew for sure I wanted to continue with this kind of research. In short, BCIs are AI-driven interfaces that translate brain activity into device commands. In other words, we use AI to make sense of the electrical signals that are measured from the brain. BCIs could be applied to find out whether a person could be cognitive overloaded, which can impact safety, attention, but also learning. Our research concerns learning. We hope that with the use of BCIs in VR, we can increase the learning curve of these pilot trainees. Although BCIs in the field of work are relatively new, a lot of research groups worldwide are working on it right now. But as far as I’m aware, no one is researching the impact of BCIs on the learning curve in VR flight training yet.’

Is a PhD something you have to discover along the way?

‘Yes, it always starts with an idea and then you have to find more information and advice. You have to find out whether your ideas are practical. It takes a long time before you can actually start a study or data collection. There are so many fields, so many devices, so many ideas.’

You started your PhD in the middle of Corona time. How was this?

‘Well, everyone was in the same boat of course. And there were a lot of online meetings. Also meetings where we could interact with other PhD candidates and sometimes even play games, which was nice. When the lockdowns were less restrictive we got to see each other in person and we could really interact. And then another lockdown came. Right now we are starting up again, but we will probably continue to work flexible.’

What role could ICAI play in this for you and other PhD students?

‘The last time I had an in-person meetup at ICAI, at the ICAI day in October, I was able to connect with a lot of people from different levels and fields. We were seated at tables with a certain topic, where we could brainstorm. I got a chance to talk to people from different universities, PhD candidates, postdocs and even professors. It was really nice to learn about other projects within ICAI. I learned as well that there are some projects that involve BCIs.’

Would you like to see more meetups specifically aimed at PhD students within ICAI?

‘Something PhD-specific is always nice to have. As a PhD candidate you have different needs than someone who is a postdoc or beyond. If you’re struggling with something in your project, data analysis for example, or the AI part of machine learning, other PhD students can think along with you and recommend something.’


On Friday, March 11, 2022, ICAI organizes the first Social Meetup for PhD students (invite only). Do you want to get to know your fellow ICAI PhD students? Sign up!

Save the date: The ICAI Day – 2022 Summer edition will take place on Wednesday June 1, 2022!

ICAI interview with Renger Jellema: Deploying AI to develop sustainable food processes to feed the world

The modern world is facing a number of converging megatrends: population growth, increasing scarcity of natural resources, and a need for the sustainable production of nutritious food. Through biotechnology DSM develops sustainable products, using nature’s toolbox, such as microorganisms. The AI Lab for Bioscience (AI4b.io) aims to accelerate this innovation process using AI technology. Renger Jellema: ‘More time to use our human creativity is going to be the most important thing we will gain from AI.’

Renger Jellema

Renger Jellema is program manager of AI4b.io, the ICAI AI Lab for Bioscience, and he is Senior Data Scientist at the Biodata & Translation group at DSM Science & Innovation.

The AI Lab for Bioscience is a collaboration between Delft University of Technology and DSM.

The lab’s first press release stated that you are the first lab in Europe to apply AI to life science and bioproduction. Why hasn’t this been done before?

‘Engineers have already been applying mathematical models in life science for decades, but now there are rapid developments in computing power and breakthroughs in AI. The combination of methods and techniques has become a unique playing field to take biotechnology, process technology, food science and even health and nutrition to the next level.’

Is this approach being used now by other researchers as well?

‘Yes, more and more biotech scientists and engineers worldwide are now launching initiatives similar to what we are doing. What is unique about AI4b.io is that we scale down, from cubic meters to nanoliters and from months to milliseconds, and not follow the more common reverse order which brings scale-up issues. We have defined five lines of research: starting with scheduling in factories, to unit operations, to automated labs, to microbial strain developments and screening to microbial cultures and health relationships in the gut.’

What can AI mean for bioscience?

‘Developments can go much faster. Because a lot of the patterns in data is already stored in the AI-models, researchers can directly go to the core of the problem. And then there will be more time for the researchers to be creative. That’s also how I explain it to colleagues who are a bit hesitant: we free up time to interpret results and come up with novel ideas. Right now about 80 percent of our time goes into managing data and doing things repeatedly.’

What kind of questions can you try to answer with the help of AI that you couldn’t answer before?

‘We want to reduce the cost of innovation while accelerating our development cycles. Mathematical models already play a good role in reducing experimental work by calculating possible scenarios in advance. What we expect is that with the help of AI we can develop better models, leading to so-called Digital Twins of microbes, processes, and factories. At DSM, for example, we produce food and feed ingredients using the process of fermentation. We grow microorganisms on sustainable, plant-derived sources such as sugar and carbohydrates. The microorganisms convert the sugar into valuable products in large steel vessels. Using advanced simulation models, we can then predict the behavior of microorganisms and interaction with their environment in such large vessels. Based on that, we can optimize these processes to become more energy efficient and produce fewer by-products.’

Can you give an example of a typical application?

‘We have developed advanced process models that can be used for large-scale fermentation vessels with a scale of 100 m3 and above. The problem with this is that calculating a few minutes of the behavior of such a vessel quickly takes a few days of computational time on a multi-core computing platform. This makes it impossible to track or monitor the process in real time. For this application, AI can be trained to represent these models – easily speeding up the calculations by a factor of 100 – acting as Digital Twins of the real fermentation vessel. The Digital Twin becomes a sophisticated digital copy of the real process.’

What can this research eventually mean to the world?

‘At DSM, we develop novel ways to produce healthy nutritional ingredients to feed the world in a more sustainable way. The Digital Twins I mentioned before, help us in the development of such processes and products, working for example toward meat alternatives using plant-based material. We combine different protein materials with ingredients such as vitamins and other micronutrients to create food solutions that taste good, have an appealing texture and keep you healthy.’

We have just set up a Launch Pad program to coach PhD students entering the job market. You have been working as a researcher in industry for quite some time. What advice would you give them?

‘Connect with scientists in companies that inspire you. If you get a chance to present your work at a company, seize that opportunity. It’s easy to shy away and stay behind your computer. But know that companies are interested in your research and are willing to help you further. Also, exploring how your research findings can be applied in practice, will improve your thought process.

Personally, I did my PhD in collaboration with Hoogovens, the steel giant now called Tata Steel. I could have stayed behind my computer and emailed them regularly to pick up the samples I needed for my modelling activities. But often I chose to visit the plant and talk to the operators who had to collect the samples. There I saw how difficult it was to take those industrial samples from the extremely hot processes and I learned to understand why the samples were sometimes not that good. As a result, I was able to change the procedures to improve my research. You have to get your hands dirty to get the best insights.’

On February 17, 2022, the AI Lab for Bioscience will talk about their current work during the lunch Meetup of ‘ICAI: The Labs’ on AI for Food in the Netherlands. Want to join? Sign up!

Not sure about your next step as a PhD student in AI? Knock on Kai Lemkes’ door

Kai Lemkes has been a recruiter in the AI domain for ten years. Since a few months he has been a matchmaker within the ICAI Launch Pad program where he coaches PhD students. Lemkes: ‘PhD students have a blind spot when entering the labor market.’

Kai Lemkes

What does the ICAI Launch Pad program look like?

‘After a first introductory meeting with the PhD students, I coach them in how they can best prepare for a job application, how to build a resume, how to present themselves on LinkedIn, et cetera. We evaluate that and then look at how this person can best present themselves and enter the labor market. We can also hold a closing meeting on request. My door is always open.’

Why is there a need for Launch Pad?

‘Many of these PhD students are at a crossroads where they don’t really know what they want next. What I encounter a lot is that students want to stay in the domain they’re already in, purely because they already know it. I recently spoke to a girl who was strongly attached to the research domain. But when I asked her to describe her ideal job, she said that she would prefer to keep improving products, give presentations and a number of things that you see much more in the commercial domain. It is therefore very important to show this group clearly what they actually choose. That’s a blind spot.’

‘The AI ​​domain has exploded in just a few years. At the moment, almost every company I work with – mainly top-500 companies – is investing in AI. For that reason, many young professionals are quickly lured abroad by companies. Foreign companies are sometimes a bit more ‘aggressive’ when it comes to recruiting talent. They proactively approach PhD students and offer them a substantial salary.’

What are Dutch companies not doing well besides less actively recruiting talent?

‘I see the recruitment process go wrong quite often. Candidates have to sell themselves in a very short time and that does not always result in a good match. Based on two or three conversations, it is quite difficult to determine whether someone is a good fit for a company for the long term. Right now, you need the luck to meet someone who likes you. If you’re having a bad day, you’re not going to look good. And especially in the technical domain you will find many specialists who are a bit more introverted or who find it less easy to present themselves, so that they already start such a process quite tense or uncertain.’

How can companies better handle this?

‘It is better to set up a process in which a company really experiences a candidate and to schedule interviews with several people from the company and not just one person. It is also a good idea to let a promising candidate speak with the whole team. Because the demand for AI specialists is so enormous right now, you sometimes see that companies present themselves as super high-tech and that a young professional later finds out that it is not that high-tech at all. Or they find out that there are no other specialists with whom they can consult. And then they can feel terribly alone. Companies need to be honest about what they have to offer.’

What is your solution to this problem?

‘I am developing the digital platform Future Impact. This should become a lively community in which students and young professionals can help each other, have peer-to-peer conversations and give ratings to companies. On this platform, companies can also present themselves and tell what they have to offer as an employer. Virtual appointments can then be scheduled for a first acquaintance. I also want to organize meetups here with people who, as PhD students, have made the step into the commercial world and can coach others in this process.’

How did you get into this job?

‘I really enjoy making matches and connecting people. I like to network and chat. I stumbled into the AI domain by accident, but I really fell in love with it. Such beautiful things happen here; startups working on zero-co2 emissions technologies, for example. AI offers so many possibilities.’

What does ICAI mean to you?

‘I am originally a commercial recruiter, but for ICAI I am really more of a coach. And actually, as I found out again, I think that’s the most wonderful job. In this role as a coach, I enter the conversation with a different intention than as a recruiter. That gives me a great deal of satisfaction. In addition, my network is growing. My highest goal is to get to know the entire AI ecosystem of the Netherlands.’

Kai Lemkes is a matchmaking expert within AI. He is the founder of several matchmaking platforms including Future Impact.

Interested as a PhD student or organization to participate in ICAI Launch Pad? Register here or send an email to kai@future-impact.io.

ICAI Interview: Improving patients’ vision and hearing with the help of AI

Umut Güçlü and Yağmur Güçlütürk run the Donders AI for Neurotech Lab together. With the help of machine learning techniques the lab tries to develop solutions that restore sensory and cognitive functions. These can be, for example, tools that improve hearing and vision, but they can also be solutions to help paralyzed people communicate or to suppress epilepsy. Güçlü and Güçlütürk: ‘We use an immensely high level of interdisciplinary exchange – with engineers, experimentalists, surgeons and ethicists – to achieve our goals.’

Umut Güçlü
Yağmur Güçlütürk

Donders AI for Neurotech Lab is a collaboration between the Donders Institute for Brain, Cognition and Behaviour, Radboud University, Phosphoenix, Advanced Bionics, Oneplanet Research Center and Abbott.

Dr. Umut Güçlü is lab manager of ICAI’s Donders AI for Neurotech Lab and is assistant professor of AI at Donders Institute for Brain, Cognition and Behaviour.

Dr. Yağmur Güçlütürk is lab manager of Donders AI for Neurotech Lab and is assistant professor of AI at Donders Institute for Brain, Cognition and Behaviour

First of all, what are your roles as lab managers?

‘Together, we are responsible for the day-to-day running of the lab. That is, we coordinate communication and research activities as well as supervising the Master students and the PhD candidates in the lab.’

The Donders AI for Neurotech Lab is working on an interesting combination: AI combined with neural implants. What kind of things is the lab working on now?

‘We develop machine learning methods for brain reading and brain writing technologies that restore cognitive and sensory function. We try to answer questions like: How can we use machine learning methods to optimally stimulate the brain via cortical implants? How can we improve the speech understanding of the users of cochlear implants? (Cochlear implants are small electronic devices that electrically convert sound into electric pulses to restore or improve someone’s hearing, red.) Another project revolves around the development of techniques for decoding and regulation of cognitive states like emotions, attention and stress.’

What are the opportunities and what are the challenges?

‘Tens of millions of people suffer from blindness and hundreds of millions of people suffer from deafness across the world. These losses can be devastating and greatly reduce one’s autonomy and quality of life. On top of that, large economic losses to society are accrued due to reduced workforce participation and burden of care. Thus far, however, previous implants have provided limited recovery of function. Hence, several crucial technological advances are required before effective, safe, permanent solutions are available to patients. Alleviating these challenges is our biggest opportunity.’

In what way can AI be used in neurotechnology?

‘AI has made huge leaps forward, allowing breakthroughs in object recognition, language processing, and even autonomous driving. With AI, a computer learns on its own from data, and develops an “answer” that can be used autonomously by a mechanical or electrical device. Here we use AI to improve the performance of brain-reading and brain-writing, enabling the automatic sensing of a signal in the outside world, or from a brain recording, that can then be directly fed to a neurostimulator for disorders of vision, movement, hearing, communication, and for the prevention of debilitating seizures. Our team is making use of the latest AI techniques in neural networks and probabilistic inference developed towards optimal signal detection, useful for the neurostimulation applications requiring rapid, and adaptive, brain-reading and brain-writing applications.’

And in what way is your lab’s approach different from other research?

‘I would say: the immensely high level of interdisciplinary exchange required to achieve our objectives. Given the scale and sophistication of our undertaking, we work together with different companies, knowledge institutes, patient organizations and an advisory board. For example, we closely collaborate with engineers who develop hardware, with experimentalists who use animal models, surgeons and physicians who perform clinical trials as well as with ethicists who keep societal implications in check.’

What kind of implants in this area are already being used by patients?

‘There are already hundreds of thousands of cochlear implant users in the world. While there is still room for breakthroughs, it is a mature technology in common use. In contrast, the technology readiness level of visual cortical implants is relatively low, having only been demonstrated as a proof-of-principle in recent studies with great leaps in the horizon. For example, one of our close collaborators Eduardo Fernández from the University Miguel Hernández in Spain recently demonstrated how an array of penetrating electrodes can produce a simple form of vision by conducting a series of experiments with a 58-year-old blind volunteer who has been blind for the last 16 years. The blind volunteer was implanted with 100 microelectrodes in the visual cortex of the brain and wore camera eyeglasses. A software was used to transform what the camera captures to how the electrodes stimulate the neurons in the brain. As a result, images comprising white points of light known as “phosphenes” were created directly in the mind of the volunteer who was able to identify lines, shapes and simple letters evoked by different patterns of stimulation.’

Interested to find out more about the research of this lab? On December 16, Donders AI for Neurotech Lab will talk about their state-of-the-art work during the lunch Meetup of ‘ICAI: The Labs’ on AI for Cognition in the Netherlands. Find out more and join!

ICAI Interview with Marjan van den Akker: Using algorithms to future-proof the Dutch public transport system

The Utrecht AI & Mobility Lab addresses complicated planning puzzles in public transport. Marjan van den Akker: ‘We are in a quadrangle with learning from data, optimization algorithms, agent-based simulations and human-centered AI.’

Marjan van den Akker (foto: GSNS, Utrecht University)

Marjan van den Akker is Scientific Director of ICAI’s Utrecht AI & Mobility Lab and is Associate Professor at the Information and Computing Science department of Utrecht University.

Utrecht AI & Mobility Lab is a collaboration of Nederlandse Spoorwegen (NS), Prorail and Qbuzz.

There are quite a few problems with public transport in the Netherlands right now. Mainly, that it’s overloaded. What kinds of problems is the AI & Mobility Lab addressing?

Marjan van den Akker: ‘One of the things we are investing is the planning for the service locations of the Dutch railways. At these locations the trains are parked when they are not running. They are cleaned there and small maintenance check and operations are carried out. These locations are in big city areas, close to the railway stations. Because of the enormous passenger growth, they are really overloaded.

And with the Dutch bus company Qbuzz we look into issues related to the energy transition and electric vehicles. Currently an electric bus cannot drive all day without charging, so you have to incorporate this in the schedule. We have to answer questions like: Do we charge often, which is good for the batteries? Or do we charge less, which makes the scheduling less complicated?’

How can these problems be tackled with AI techniques?

‘What we see at the service locations of the NS is that the problems are so complex that we need a hybrid approach. We have to combine optimization algorithms and learning algorithms. Another challenge is that all these planning algorithms are incorporated in decision support systems. They are used by humans in two ways. One way is on a strategic and policy level where people use the algorithms to estimate capacities of the transport system. The other way is in the actual operations where human planners need to work together with the system. The system does all these complicated calculations and suggests solutions, but it may happen that the human operator has to alter the plan because of a sudden change.’

Is this approach unique in the Netherlands?

‘Yes, to have all these different approaches in one computer science department is rather unique. Most places in the Netherlands use Operation Research in a mathematical way. But we use a hybrid approach of computational AI and Operation Research algorithms.’

Can you already tell us something about the lab first results?

‘We are a long-term lab and it’s founded on the bases of research that has already been running for some years. Four years ago one of our PhD students, Roel van den Broek, started on an algorithm for the planning of the service locations of the NS. The NS is currently running a pilot with this algorithm and intends to take it into use. This algorithm plans everything on the service locations: where the arriving trains have to be parked, when they go into the maintenance facility and the cleaning platform and which train units have to be coupled or decoupled. And that all has to be arranged in this highly packed area.’

You have been a technology consultant at the National Aerospace Laboratory for five years. Does that experience help you in working with companies?

‘Yes, I think I have more insight in the application domain. The goals of companies are a bit different from the goals of the scientists. A company of course, is mostly interested in the results and not in the paper that we write.’

What are the ambitions of the lab?

‘We want to extend in the field of logistics. We have conversations with new companies that are involved with mobility as a service. For example a company that organizes the use of shared cars and bikes and also gives travel advice. We try to connect as much as possible with all kinds of organizations in the new changing mobility area.’

Utrecht University has founded the AI Labs, of which the Mobility & AI Lab is part, and is ambitious in the field of innovative AI techniques. Can you tell us something about these plans?

‘Labs are a good instrument to achieve research. The UU started out with the Police Lab a few years ago, and that was very successful. So we are founding AI labs now in all kinds of areas. Besides the mobility theme, we are working on setting up labs in the fields of sustainability, health, media and the humanities. In Utrecht we can provide multidisciplinary research, in a way that a university of technology couldn’t, because we have more possibilities to take a human-centered approach.’

On November 18, 2021, the Utrecht AI & Mobility Lab will talk about their current work during the lunch Meetup of ‘ICAI: The Labs’ on AI and Mobility in the Netherlands. Want to join? Sign up!

ICAI Interview with Ben Luijten: improving ultrasound images with the help of deep learning

Ben Luijten is halfway through his PhD research at e/MTIC AI-Lab and is already working with his team on a number of prototypes of Philips ultrasound devices incorporating their algorithms. Luijten: ‘We work really close to the clinicians right now to find out what the best image quality is for them.’

Ben Luijten

Ben Luijten is a PhD Candidate at the Biomedical Diagnostic Lab at Eindhoven University of Technology and is a member of ICAI’s e/MTIC AI-Lab.

e/MTIC AI-Lab is a collaboration between Eindhoven University of Technology, Catharina Hospital, Maxima Medical Center, Kempenhaeghe Epilepsy and Sleep Center and Philips.

What can artificial intelligence and deep learning techniques mean for ultrasound imaging?

‘At the core of our research, we are trying to maximize image quality of ultrasound images. Ultrasound imaging has been around for almost fifty years now. It’s a fantastic technique that makes it possible to convert sound reflections into images, and take a look inside the human body in real-time. Technicians however, have always struggled with image quality. MRI and CT scans have a very high image quality, but they don’t give real-time feedback, and are very expensive. We are now trying to improve ultrasound image quality with the use of AI.’

Why is real-time image processing so hard?

‘Our devices have to operate within a fraction of a second. Ultrasound imaging needs at least thirty frames a second, and sometimes even up to a thousand frames a second in the case of blood flow measurements for example. In order to process all this information in real-time, the reconstruction algorithms have to be very small and lightweight in order to run on medical devices.’

What is your approach to this problem?

‘We look at the algorithms early on in the signal chain, so before the image formation. That way we are close to the signal processing. The signal processing is everything that happens in between the measurement and the image formation; the sampling of the signal, putting it into a digital form, filtering it, etc. Since these techniques are already well understood, we try to implement intelligent solutions that stay close to these conventional steps. In doing so, we can improve the image quality in a robust way, with relatively compact neural networks, and a small margin of error.’

How far have you already come with improving the image quality?

‘Well, the first question here is: what actually is the best image quality? Sometimes, what we as technicians find really good images, with very high contrast and resolution for example, the doctors don’t like at all, because they don’t see the same things as they did before. So we work really close to the clinicians right now to find out what the best image quality is for them. At this point we already have some good working AI algorithms that we started to implement in prototype systems.’

How will the next generation of ultrasound devices look like?

‘We want to head into the direction of image processing with small, portable ultrasound devices. That way a doctor could go to a patient’s home to do some basic scanning with lightweight AI running on his or her smartphone. And more complex processing could be done in the cloud. This could be a solution to the triage problem we saw during Covid for example. We did a side project on automatic assessment of Covid-severity, based on ultrasound scans of the lungs. And it worked pretty good. Eventually, the development of these portable ultrasound devices could change the way we use diagnostic tools.’

The e/MTIC AI-Lab promises ‘to provide a fast track to high-tech health innovations’. What does this ‘fast track’ look like?

‘e/MTIC connects a lot of collaborations and people in one lab. e/MTIC Lab tries to create a knowledge hub to minimize the fraction between all the research teams, the industry partners and the hospitals. It really lowers the boundaries of getting in touch with each other. If I have a question on a certain image, they can connect me to a doctor who can help me with that specific image or question.’

What’s next for deep learning in ultrasound imaging?

‘We are going to a certain point where the difference between conventional signal processing and deep learning becomes smaller and smaller. But within medical imaging we’re not likely going towards AI that can do everything. Instead, the focus will be on assisting the doctor in his or her work. In our case, this means developing fast, light-weighted neural networks, that get the most information out of the ultrasound measurements as possible.’

On September 16, 2021, Ben Luijten will talk about ‘Deep learning for ultrasound signal processing’ during the Lunch at ICAI Meetup on Deep Learning in the Netherlands. Want to join? Sign up!

ICAI Interview with Martijn Kleppe: gaining insights much quicker by combining AI and Humanities

Martijn Kleppe is a trained historian who collaborates with AI scientists. Kleppe: ‘When I was doing research as a historian, I could analyze twenty books in a month. For the computer this is a matter of seconds.’

Martijn Kleppe

Martijn Kleppe is one of the founding members of Cultural AI Lab and the head of the Research Department of the KB National Library of the Netherlands.

The Cultural AI Lab is a joint effort of the heritage institutions Rijksmuseum, Institute for Sound and Vision, the KB and knowledge institutions CWI, KNAW, UvA, VU and TNO.

What problems within the humanities can be solved with AI?

‘The biggest challenge that we face within the humanities is scale. There is a shift happening right now within historical research from close reading, which we have done for centuries, to distant reading, where we use a computer to detect patterns in all sorts of humanities data. In books, newspapers, television programs, artworks, social media outlets etcetera. This is the biggest opportunity that we have right now, but also the biggest challenge because we have to rely on other competences to make this kind of research possible.’

How is the interest in AI from the cultural world?

‘It is really gaining momentum right now. There is an ecosystem evolving with partners from the culture and media domain – like the Rijksmuseum, the National Archive – and the creative industry, that are interested in applying AI within their services or processes. And several members of our lab recently founded the working group ‘Culture & Media’ within the National AI Coalition with all sorts of cultural and media partners.’

Does the humanities also influence AI?

‘Yes, it creates new academic research questions. Most of the algorithms within the AI domain have been trained with new and high quality data. But in the datasets of the heritage institutions there is 200 year old data. Digitized newspapers from the end of the nineteenth century with a very low quality for example. And newspapers from colonial Indonesia and Suriname with a completely different vocabulary. Bringing in those kinds of datasets within the AI domain offers new perspectives and questions on polyvocal data. How can we handle these kinds of data and how can we improve them? My experience so far is that the computer scientists involved in our project love these new questions.’

At the ICAI meetup on July 8, the lab will speak about contentious words in cultural heritage collections. How does the lab approach this?

‘Handling issues like that, is the essences of our research. We try to answer the question: How can you detect bias in descriptions of artifacts of museum collections? And can you also help the museum by giving them suggestions for other kind of words? Especially last year, with the Black Lives Matter movement, we saw how relevant these questions were. How do we deal with the past? It is a technical, but also really a societal and ethical challenge.‘

Boek uit de KB collectie

As a historian, what attracts you in AI?

‘I have to ask new types of questions. Instead of doing source criticism on books, newspapers or television programs, now I have to do source criticism on algorithms. At the KB we have the Delpher.nl platform for example, where you can search in millions of digitalized texts from Dutch newspapers, books and magazines. In order to search efficiently, you have to understand the basis of the algorithm behind it. What I also really like about the AI research is the teamwork. Traditionally humanities scholars are more solitary researchers. But to be able to collaborate with other disciplines you have to be vulnerable and develop yourself.’

Does it happen that you get a new perspective on heritage collections because of the AI research?

‘Yes, for sure. During my PhD research I wrote an article about the first moment in time when a Dutch photograph was published in a Dutch newspaper. That research was based on manually going through a selection of newspapers. But then four years later I participated in a research project with a historian who was one of the firsts to start applying Computer Vision on historical newspapers. He run an algorithm that went through all the newspapers and immediately said: ‘Martijn, you were completely wrong. The first photograph was published earlier than the moment you mention in your paper.’ That was fantastic. Science is always about gaining new insights. When I did my research, those techniques did not exist yet. We now gain insights much quicker than we did before.’

On July 8, 2021, Cultural AI Lab will talk about making AI ‘culturally aware’ during the Lunch at ICAI Meeting. Want to join? Sign up here.