Currently, she’s a research scientist at Meta. Before that, she was a postdoctoral researcher in the Computer Vision and Machine Learning department at the Max Planck Institute for Informatics (MPI-INF).

After being exposed to computer vision during her bachelor’s in Russia by coincidence, Anna decided to stay in the field throughout her entire education. Upon completing her PhD in Saarbrücken, she was awarded the Otto Hahn Medal for her thesis “Advancing Image and Video Recognition with Less Supervision,” in which she explored how to get good results on unseen data with minimal labeling.

In this interview, Anna talks about her award-winning thesis, her experience as a researcher at MPI-INF and as a woman in computer vision, and her advice to aspiring computer scientists.

What drew you to computer science and more specifically, to computer vision? 

It was almost an accident, to be honest. I actually planned to go into mathematics. In Russia, we have an international unified exam, and I was looking at the list of subjects I needed to pass. Russian and mathematics are mandatory, so you don’t need to choose them. In addition to those, I only had physics, while everyone else in my class was choosing more subjects. So I thought, why shouldn’t I? That’s when I decided to take computer science. There was nothing I didn’t like about it — it was almost math, but slightly different, and I found that I could learn it quickly. After getting my exam results, I thought, why not choose computer science as my major? 

When I started my bachelor’s, I was the only one in class who didn’t know how to program. That was slightly alarming, but in the beginning it was mostly math anyway. 

After my first year of studying, I ended up in a summer camp — again, by accident. (Lots of happy accidents!) We worked on a quadrocopter (drone) in four teams, with each team responsible for a different section. For instance, one team developed the app’s web page, while our team was responsible for computer vision. I did camera calibration in C++, which I didn’t know at the time, and it became my task for the entire camp. I had a lot of fun doing it and fell in love with computer vision. I decided to stay in the field, and in the end, it worked out! I spent two years in a computer vision lab during my bachelor’s, and when I moved to Germany for my master’s, I joined another computer vision lab as a HiWi.

I like working with images to understand how computers interpret and extract information from what we see — which is, in memory, actually just numbers representing pixels. It’s intriguing that we can reason more and more just from numbers. This is fascinating. The field is developing so fast. It’s incredible to be part of it and stay on top of how these systems work: what kinds of algorithms they use, how they are trained, and what their limitations are. I wouldn’t necessarily say it’s more interesting to be in the field right now, but it’s certainly different, because it’s applied in the real world more than ever. Right now, computer vision is deep learning, which has increasingly been integrated with language models over the past few years.

Currently, you can just upload images to a model and ask, ‘What is in this image?’ That’s related to my field and what I’m doing, because the model needs to understand the context of the image or video. Developments like these are extremely motivating to stay in the field and keep on pushing the research boundaries. The pace of the field has been crazy in these past few years. Nobody knows what’s coming because there’s always something new happening. You see papers coming in the arXiv every day.

You were awarded the “Otto Hahn Medal of the Max Planck Society” for your thesis, “Advancing Image and Video Recognition with Less Supervision”. What piqued your interest in the topic and can you tell us a bit more about your thesis?

Absolutely! The topic is about how we can learn from data. 

To give you more context, I started my path in computer vision research with unsupervised learning. Imagine you have a bunch of data, a bunch of videos — and you’re trying to understand how coffee is made. There are actions like picking up the cup, putting on the cap, stirring sugar, pouring water, and you want to recognize all these actions — but in a way that doesn’t require supervision in the first place.

Supervision is very hard to obtain. It’s very multifaceted and comes with limitations; you can’t label everything in the world. There are a lot of underrepresented classes, and things change all the time. For example, today we sit on these chairs, and in five years they might look completely different, but their purpose will be the same: we still sit on them. In this case, you would need to re-label and add these new chairs to your model, so it can recognize them. 

There are problems with labeling as well. Going back to the chair example, you can label the whole chair, or just the arm or back, depending on the granularity of your labeling and the downstream task you want to solve. 

What I was primarily interested in was how we can exploit the data in different scenarios. When we talk about video understanding, image understanding, or multi-modal understanding, the question is how we can generalize using only a subset of absolutely unlabeled data. Can we achieve good results on unseen data? On test data? That was my topic, in a nutshell.

Regarding my interest in the topic, I think I found common ground with my supervisor. I wanted to work with less supervision. When I started my PhD, we began with few-shot learning, where you’re given very few examples and need to learn your representations from them — and you then model based on these few samples. 

So we started there, and because I like videos, I steered back to working with video data. Fast-forward to later, I was working with large-scale, unlabeled data, focusing on how to get better representations and improve datasets using LLMs. 

It was always about less supervision, mostly with videos but sometimes with images, and about how different learning dynamics influence the kinds of models we actually get. The goal was to understand the internal structure of algorithms. It was also very interesting to see exactly what a model learns and what influences the structure of its representations. This focus was always there: learning with less data and overall less supervision. And then we explored different directions along these lines.

Your thesis mentions that fully supervised models are often impractical in real-world applications. Which fields or industries do you think struggle the most with this necessity for data labeling?

I think any field would be limited. Especially when you have a pre-defined, structured pipeline, like in car manufacturing. There, you have details that always have the same size and shape. But if you don’t have a window, then the outdoor lighting doesn’t make any difference — you can set up the lighting at a fixed point so everything always looks the same. When that happens, robots or algorithms can manipulate objects without much difficulty, because you can define precisely where and how things should be done. 

So the factors and the objects are fixed. It’s a sealed, perfect environment where nothing changes. Whenever something starts to change, though? You’ve got a problem. Imagine you have a table and your task is, say, recognizing objects to help you make coffee in a VR setup (like those from Apple or Meta). Your coffee-making mechanism needs to recognize the cup, the steamer, the spoon, the coffee itself — essentially, everything has to come together. Afterwards, the system should also suggest the actions you’ll need to take. 

Maybe the system doesn’t work if there’s too much or too little light, so it should be able to tell you to open the window, turn off the light, and so on. Keep in mind that this is still an example with rigid objects.

Let’s think of a plastic bag instead, which changes shape constantly. Imagine filling it with powder or liquid — a common scenario in medical applications. Sounds easy, you just take the bag and fill it, right? No! This is actually very complex because a plastic bag isn’t rigid and is constantly changing. There’s also the durability factor. We have sensations that tell us when the bag is under too much stress, close to ripping, and how much force we’re applying to carry it. Our senses give us that feedback — but robots don’t have that. For them, controlling that is extremely hard. 

Essentially, take any application where you have some degrees of freedom, and you’ll see that it’s very hard to label everything. Take autonomous driving, for example. 

Of course, large teams at every car company create proprietary datasets (which aren’t available to academia because they’re not open source). But still, they can’t label everything, as some things are simply unpredictable and it costs a lot of time and money. 

However, generally speaking, it’s not sustainable for every industry or company to collect its own datasets and guarantee high quality. You often need three annotators per data point, not one. You need to be consistent throughout the entire process, and ensure the annotators do a good job and that there aren’t mistakes involved. It’s very common to have mistakes in datasets, because labeling is hard. So essentially, name the field —and you will definitely need methods that rely on less supervision.

Fundamentally, the best approach that I see is to start with a small dataset with very high-quality labels and then propagate this to unlabeled data. Perhaps it’s not an approach that works absolutely without labels, but for some downstream tasks, some guidance is still needed in terms of what labels are expected and what kind of supervision is required. The model can catch up gradually.

However, fully labeling everything is unreasonable, because the world is constantly changing. Then you would need to constantly update and retrain models. It’s much better and effective if a model can pick up on something new. Models should be able to recognize new concepts and integrate them into their representations over time.

And currently, can this approach be used for complex tasks?

I think so. It depends, because you can’t always take something and apply it to completely different data — it might not work. You need to understand the internal structure of the data and how to apply it. There are also additional biases we have as humans, in terms of how things work and what to expect.

Let’s think about an instructional video on making coffee. You know that you’ll first get a cup and then pour water, not the other way around. One action comes first, we know that. This kind of bias can also be observed in other data structures, especially biological or molecular. There are some connections that should be there and some that shouldn’t. You can explicitly or implicitly impose this on a model while it’s learning, and thereby help it recognize things.

What were some challenges you faced during your research and how did you overcome them? 

Every PhD has certain ups and downs. Most of the time, it’s the moments when you’ve been trying to figure out something and suddenly, nothing works and you have no idea what to do next. There are different ways to overcome this. What worked for me was taking a step back — going for a walk, overall, relaxing.

Sometimes when you constantly try to make a problem work and it still doesn’t — you get lost in small details and can’t think of new solutions. When that happens, you need to do something absolutely different — distract yourself for a bit. Read papers, talk to people, sleep, whatever you do: don’t think about the problem. When you’re done, come back and try something different.

It helps! Sometimes it might take more time — maybe weeks, maybe months — but it happens eventually. This is the main PhD challenge for everyone, I think. Of course it’s always frustrating when something you think will work doesn’t, but part of the process is learning how to deal with this. You will have ups and downs. It doesn’t get easier, to be honest, but you learn how to handle it.

In addition to that, for me, starting my PhD during Covid was a huge challenge. I essentially moved to a new city, knowing no one, only a month before Covid hit. I went to the office for a month, and then it closed down. Socially, that was pretty hard. It’s interesting, really — when you’re frustrated and alone, it only adds more pressure.

I did an internship right at the beginning of my PhD as well. Thinking about it now, I think it was too early. It didn’t work out in the end. My manager left as soon as I joined, the project itself fell apart, and although I had the opportunity to continue, I think I made the right decision to say, ‘Okay, I’m not doing this anymore.’ I feel like it takes some courage to tell your supervisor that. Afterwards, I moved to another project. 

I would say it’s important to know when to step back sometimes. In this particular case, I’m quite proud of myself. 

Before working at Meta, you were a postdoctoral researcher at the Max Planck Institute for Informatics at the Saarland Informatics Campus. What were you working on?

I was involved in several projects. I helped PhD students, supervised master’s students, and also worked on my own research. One of my projects focused on understanding attention in diffusion models, which are generative models that create images or videos, such as DALL·E or Sora. You go to the app or the webpage, send a prompt that goes, ‘I want to have a cute cat,’ and the model generates it.

Diffusion models can be used for many different tasks, from image generation to extracting internal representations for applications such as segmentation. I specifically studied attention layers, which are key building blocks of these models. They are where communication between an image and the model’s internal representations takes place. Conceptually, it’s an operation — like multiplication, but far more complex — and we studied how it functions inside the diffusion models, how it affects image generation, and how it shapes the representations learned by the model. At the time, we were targeting a specific segmentation task. We had found a mechanism that amplifies certain attention regions in the internal representations, improving segmentation performance. Essentially, we discovered how to adjust the attention operator and its outputs without retraining the model.

Although the focus of my research remained on computer vision, we were increasingly incorporating language as well. We explored how images or videos could be combined with language to improve internal representations or enable reasoning about visual scenes. This included tasks such as answering questions about visual content, describing relationships between objects in an image, and incorporating additional modalities like audio, depth estimation, or optical flow.

This research was part of a collaboration with Google. Our group at the Max Planck Institute, together with Christian Theobalt’s group, participated in the VIA collaboration. I worked closely with researchers at Google, met with them regularly, and co-authored research with one of the collaborators.

What set the Max Planck Institute for Informatics and SIC apart for you in terms of research opportunities, as well as academic and professional development?

First off, during my interview with my supervisor, I immediately felt a connection. I think it’s super important to see if things click with your supervisor before starting a new position, so I was really happy that was the case. Our communication felt established from the start. 

Oh, and they have a great coffee machine! I’m telling you, I had an internship in Paris — I didn’t expect this from France, but the coffee machine? Quite bad. At MPI, though? I even learned how to make coffee art during my PhD!

Jokes aside, the more time I spent there, the more I learned about the Computer Vision and Machine Learning department and how it was structured. The infrastructure was impressive compared to any other place I had seen in academia. Not just academically, but also in terms of the clusters and setups we used to train our models. There was a dedicated IT department — handling server management, laptops, devices — so that everything ran smoothly. 

I was surrounded by truly inspiring people and there were plenty of possibilities to learn, explore, and collaborate. In our department, we had subgroups that worked on very different topics, and you can learn a lot from that. If you were interested, you could even collaborate. Overall, it was quite different from other places because we had three or four professors in close collaboration under this department.

We also had retreats twice a year, not just within our group but jointly with other groups across Germany. They were a great opportunity to meet people, connect with professors, and even make friends. You got familiar with what the other groups were working on while building nationwide connections.

Most importantly, compared to my friends at other institutions, we had less teaching responsibilities. I found that genuinely amazing — we could concentrate on research completely, which is exactly what you need during your PhD. That was a huge factor for me when I was choosing MPI-INF.

What advice would you give to students starting out in computer science, machine learning or computer vision, and to prospective SIC students – especially young women?

First things first, you need to build up your portfolio. When I first started as a HiWi, I was pushing to take on projects or help with existing ones; that’s how you learn. 

When you’re looking for a PhD, there are basically two options. First option: trying to work with a well-known, established professor, which can be great — but you have to understand you might not see them much because they’re busy. In this scenario, while it would also depend on the group structure, you’ll most likely be working quite independently, which can be challenging.

Your second option, which is very valuable but harder to find, is finding a fresh professor. They’re usually enthusiastic, more involved in your projects, and you can learn a lot from them. It can be slightly easier to get a position with a fresh professor, because there are fewer people applying to them. There are pros and cons to this though — firstly, as I said, it’s very hard to find. And you have to consider the institution and the resources it has. A fresh professor at a reputable institution is ideal. Fresh professors at less-established places, however… I’m not saying you can’t be lucky, but you could also be unlucky. It’s a hit or miss, you know? In a reputable institution, a fresh professor is more likely to give you attention and guidance. Senior professors, on the other hand, are less available because they often travel a lot and don’t attend all meetings — which is totally normal. Like I said, they’re busier in comparison.

If you feel too conflicted, asking peers for recommendations can help — although if they’re also master’s students, they might not know much themselves. All I’m saying is, it’s tempting to target the big names, I know, but it may not always be the ideal choice. Take your time and choose your destination wisely. I think I got really lucky with MPI-INF Saarbrücken. 

Another thing, and this is very important: don’t do ten projects at the same time — concentrate on one! One project is more important than ten simultaneously. It rarely works. I only know one person capable of doing that, and mind you, he’s a genius — but even he can only handle five at a time. Everyone else fails when trying to juggle more than one, myself included.

I was initially just a member, but now I’m on the board of the Women in Computer Vision workshops. We publish at major conferences like CVPR (Computer Vision and Pattern Recognition Conference), ICCV (International Conference on Computer Vision), and ECCV (European Conference on Computer Vision) rather than in journals.

From my experience, I would recommend women in computer science to simply reach out to other women — it’s a great way to meet people who can understand and support you. In our field, there’s a culture of guiding or helping out others when they’re at times of uncertainty — this applies to all genders, of course. But since women are still underrepresented in computer science, especially in computer vision, this support can be especially valuable.

As a matter of fact, many professors are trying to recruit more women for PhD positions, but it’s hard because there aren’t many applicants. So my advice is simple: just apply, that’s it! If you’re interested in someone’s research and would like to reach out, do it! Don’t be afraid to take the first step.

Interested in the Computer Vision and Machine Learning department of the Max Planck Institute of Informatics? Click here to visit their page.

Woman in Computer Vision? Visit the website of Women in Computer Vision to find out more about their activities!

Editor:
Saarland Informatics Campus Team
Email: yagmur.akarsu@uni-saarland.de

The following text has been machine translated from the German with no human editing.

Born in Eberbach am Neckar, he first completed an apprenticeship as a carpenter and joiner and went on to obtain an Ing. grad. in wood technology from the Rosenheim School of Engineering. He subsequently obtained his Abitur through adult education at the renowned Braunschweig-Kolleg and studied mathematics and physics at the University of Göttingen, as well as computer science at the University of Essex in the UK. There, he was awarded a Master of Science in Computing in 1972, obtained his doctorate in Artificial Intelligence in 1976 with a thesis on unification theory, and then returned to Germany.

Since then, Jörg Siekmann has worked first as a research assistant and, from 1980, as a university assistant at the Institute of Computer Science at the University of Karlsruhe, where he established a research group on artificial intelligence (AI) in the field of deduction systems and, together with others, founded the influential Collaborative Research Centre 314 on AI. In 1983, he was appointed to Germany’s first professorship in Artificial Intelligence at the University of Kaiserslautern, where, together with several colleagues, he founded the German Research Centre for Artificial Intelligence (DFKI). The Springer series ‘Lecture Notes in Artificial Intelligence’ (LNAI), which he founded, gained international prominence.

In the summer term of 1991, Siekmann accepted a post at Saarland University and, until 2006, held a dual role as Professor of Computer Science and as Head of Research at the German Research Centre for Artificial Intelligence (DFKI). On the Saarbrücken campus, his roles included serving as spokesperson for Collaborative Research Centre 378 ‘Resource-Adaptive Cognitive Processes’; in 1995, as a visiting professor at JiaoTong University in Shanghai, he initiated the cooperation with our university; together with his second wife and colleague Erica Melis, he served as scientific director of the ‘Centre for e-learning Technology’; and he was a senior professor and coordinator for digital education at Saarland University.

The honouree has made an outstanding contribution to establishing ‘Artificial Intelligence’ as a field of research in Germany and to creating the organisational framework for this discipline within the ‘German Informatics Society’, which awarded him the title of ‘Fellow’ in 2002 and later named him one of the ten most influential AI scientists. Overall, he has made significant contributions to AI research, taken on extensive responsibilities within the academic community, and advised research groups, governments and industrial companies at both national and international levels. His body of work comprises around 900 publications in his fields of research: deduction systems, proof planning, unification theory, e-learning for mathematics, as well as Buddhism and Artificial Intelligence.
Unfortunately, for health reasons, the honouree is currently unable to host public celebrations.

Further information: 
Dr Wolfgang Müller, University Archives
Email: wolfgang.mueller(at)uni-saarland.de, dr.wolfgang-mueller(at)t-online.de 
 

Presseeinladung zum Projektabschluss: 
To mark the conclusion of the Escade project, funded by the Federal Ministry of Research, Technology and Space, the team will present how AI can make a smaller environmental footprint while also giving small and medium-sized enterprises access to powerful AI models. The event will take place on 30 July from 10 a.m. to 1 p.m. at the DFKI on the Saarbrücken campus (D3 2) and is open to the public.

The following text has been machine translated from the German with no human editing.

Fast jeder nutzt heute auf die eine oder andere Weise Künstliche Intelligenz. Was nicht jedem bewusst ist: Auch die kleiAlmost everyone uses artificial intelligence in one way or another these days. What not everyone realises is that even the smallest response from a chatbot consumes energy and resources. Training and running AI models using vast amounts of data consumes hundreds of terawatt-hours worldwide. On a global scale, all of this leaves a massive ecological footprint. And with the rapid development of the technology, demand is set to rise sharply. New data centres will seal off vast areas of land, as will new power stations – including those fuelled by fossil fuels – which will have to supply the additional energy required. CO₂ emissions will rise just as dramatically as the water required for cooling.

A consortium led by Professor Wolfgang Maaß, who conducts research at Saarland University and the German Research Center for Artificial Intelligence e Artificial Intelligence (DFKI), is working to counteract this trend and make artificial intelligence more energy-efficient. For three years, the consortium has been investigating various methods and developing and testing new technologies. The researchers are presenting their findings at the conclusion of the project.

Compressed AI requires almost 90 per cent less energy

On the one hand, the team is focusing on smaller, more needs-based AI models to curb AI’s energy consumption and conserve resources. Today, AI uses huge data models. A chatbot, for example, utilises the entire data model – comprising trillions of parameters – to generate its response: figuratively speaking, it searches an entire library rather than just the books containing relevant content. The researchers have therefore developed AI models in which irrelevant parameters are not processed in the first place, making them more energy-efficient.

To do this, they filter out the knowledge that is truly necessary for the respective task from large teacher models and create bespoke student models that are up to 90 per cent smaller. “We’re achieving good results by compressing the AI models – in other words, making them smaller and more efficient. In our test runs, we were able to demonstrate that the student models deliver comparable performance whilst using up to 89 per cent less energy,” says Sabine Janzen, a postdoc in Wolfgang Maaß’s team. The leaner AI models, tailored to specific use cases, operate without the need for extensive infrastructure. “This makes powerful AI models accessible even to small and medium-sized enterprises, something that was previously impossible simply because of the size of the models,” says Janzen.

Automatically selecting the best AI model using 40 per cent less energy

For AI models that process and generate digital image data, the researchers use a different method known as ‘neural architecture search’. This method automatically identifies the best architecture for artificial neural networks. The team was able to demonstrate that they can reduce the size of the models by just under 90 per cent and cut energy consumption by 40 per cent. What’s more, the models do not suffer any loss of performance. “We were even able to improve the model’s accuracy with this approach,” says Sabine Janzen.

In machine learning using artificial neural networks, the learning processes are similar to those in the human brain. Whilst the human brain is a master of energy efficiency – having continuously optimised itself through evolution and processing information very efficiently – its artificial counterpart, with its efficient algorithms, requires an enormous amount of computing power and electricity: Artificial neural networks are still painstakingly assembled by humans today and fine-tuned until they deliver good results. “We are automating this process using neural architecture search. In doing so, we test various network structures and optimise them further so that the models are powerful and efficient, but cost less,” explains Sabine Janzen.

Scrap Sorting Test Case

In order to test such more energy-efficient AI methods on an industrial scale, the researchers have been collaborating with SHS – Stahl-Holding-Saar since 2022 as part of the Escade project.

SHS has developed a highly capable AI model that automatically classifies steel scrap and uses camera images to identify which type of steel scrap is being delivered to the steelworks site. Since 2024, SHS has been using its own in-house, optimised AI model to classify steel scrap: using camera images, the AI recognises the different types of scrap, enabling the steel scrap to be sorted by type before being used in production.

The aim of the Escade project was to make an AI model with roughly equivalent performance even more energy-efficient. The research team developed a high-performance visual AI model for steel scrap classification. This new model was compressed in such a way that it operates in a compact, energy-efficient manner whilst delivering performance similar to that of an AI model created using traditional methods. This makes the visual computing process more energy-efficient. To achieve this, the partners first trained the model using the complete data set and all relevant information, and then compressed it using knowledge distillation and automatically assembled neural networks. The techniques developed can, in particular, be applied to other visual AI models and can thus contribute to energy savings on a wider scale.

Further potential for savings in sustainable data centres

Furthermore, on 30 July, the consortium will highlight potential savings for data centres. Together with its partners, the Saarbrücken-based research team has developed a concept and recommendations for energy-efficient AI, which will enable data centres and AI users to plan more effectively and identify inefficient processes. To this end, the team has developed a tool that enables reliable forecasts of the exact energy consumption and costs of AI models. “This tool makes it possible, for example, to schedule processes that require high computing power for times when the price of electricity is low. Until now, decision-makers have found it difficult to estimate how much energy specific models will consume, which is why it is challenging to plan ‘ ’ in a cost-effective manner,” explains PhD student Hannah Stein, who is researching energy-saving AI methods.

Neuromorphic chip technologies

In addition, the research consortium is working on neuromorphic chip technologies in the field of hardware; these are microprocessors that also mimic the functioning of the human brain. “Our findings in this hardware area suggest that this technology also operates significantly more energy-efficiently than conventional chips: in our tests, they are already up to six times more efficient. But further research is needed here, and we need more time for that,” explains Sabine Janzen.

On 30 July, the research consortium will present its project findings on sustainable AI data centres and resource-efficient AI algorithms, including through demonstrations. Interested parties, in particular data centre operators, technology providers and companies, are invited to attend.

Registration and programme for 30 July: https://escade-project.de

Background

The ESCADE (Energy-Efficient Large-Scale Artificial Intelligence for Sustainable Data Centres) project was funded by the Federal Ministry of Research, Technology and Space (BMFTR) with around five million euros over a three-year period as part of the ‘Green Tech Innovation Competition’.

The project consortium comprises, in addition to the research team led by Professor Wolfgang Maaß as coordinator (German Research Center for Artificial Intelligence DFKI and Saarland University), the Technical University of Dresden, Bielefeld University, the Central German Data Centre NT Neue Technologie AG (NT.AG), SHS – Stahl-Holding-Saar GmbH & Co. KGaA, SEITEC GmbH, the Austrian research organisation Salzburg Research, and the subcontractors eco2050 Institute for Sustainability, SpiNNcloud Systems GmbH and elevait GmbH & Co. KG.

The project is managed by the German Aerospace Centre.

For enquiries, please contact:

Dr Sabine Janzen: Tel: 0681- 8 57 75 – 269, Email: sabine.janzen@dfki.de

Hannah Stein: Tel: +49 681 302-64739, Email: hannah.stein@iss.uni-saarland.de

https://escade-project.de

Every year, millions of deaths worldwide are linked to antibiotic resistance. At the same time, developing new antibiotics is particularly risky, as many drug candidates are eliminated in early development phases due to undesirable side effects or lack of efficacy.

OptimAIze addresses the urgent medical need for new antibiotics and the specific development challenges posed by antimicrobial resistance and high preclinical failure rates, often caused by an unfavorable balance between efficacy and toxicity. The goal is to develop and apply generative AI methods to target the modification of drug candidates to increase their efficacy and reduce cytotoxicity—that is, the potential damage to human cells. A particularly innovative aspect is the integration of mechanistic knowledge into the AI.

“Artificial intelligence opens up enormous opportunities for drug discovery. At the same time, purely data-driven models reach their limits, especially when only limited or unbalanced data is available. That is why, in OptimAIze, we rely on a combination of AI and mechanistic expertise. This allows us not only to make more precise predictions but also to better understand why certain molecules are effective or toxic.”

Prof. Dr. Verena Wolf, Head of Neuro-Mechanistic Modeling, Project Coordinator OptimAIze

At the heart of the project is a novel closed-loop learning cycle that combines artificial intelligence and experimental validation. High-resolution cytotoxicity data are analyzed using modern AI methods. In the process, the models not only learn to predict the efficacy and tolerability of molecules but can also specifically suggest new drug candidates, which are then tested experimentally. The results are fed back into the AI models, continuously improving their predictive power.

The added value of OptimAIze lies in its integrated AI optimization, which combines knowledge across AI and computational chemistry to efficiently propose molecules with improved antibiotic activity and lower toxicity that can be synthesized and tested immediately.

To this end, the consortium brings together complementary expertise: DFKI develops methods for explainable and generative AI; Saarland University contributes expertise in drug design, language models, and single-cell analyses; HIPS handles the chemical synthesis and optimization of the candidates; and smartbax GmbH provides exclusive data and drug programs from industrial antibiotic research. 

In addition to developing specific antibiotic candidates, OptimAIze also aims to make innovative AI methods and software tools available to the scientific community. The algorithms developed will be published as open source—as far as possible—and will thus support further research projects in the long term in the fight against antibiotic resistance.

With a duration of three years, OptimAIze aims to set new standards for AI-supported drug discovery and make a significant contribution to combating antimicrobial resistance. The project is funded by the Federal Ministry of Research, Technology, and Space under the funding guideline “Application of Artificial Intelligence in Drug Discovery.”

The university’s project, Saarland Future Minds Programme in Quantum Technologies and Artificial Intelligence, was selected from more than 70 applications in a nationwide competition organized by the German Academic Exchange Service (DAAD). The DAAD is funding the project with around 709,000 euros through the end of 2029.

The following text has been machine translated from the German with no human editing.

With the recently launched Centre for Quantum Technologies, the state and Saarland University have underlined their aim of establishing this key future technology in the Saarland – not only within the research landscape but also beyond it, for example in spin-off companies. The German Academic Exchange Service (DAAD) is now supporting this initiative with comprehensive funding to attract students and doctoral candidates, who are to be specifically recruited and retained.

The “Saarland Future Minds Programme in Quantum Technologies and Artificial Intelligence” (FMP Quantum AI) highlights Saarland University’s status as a leading national centre for attracting outstanding young talent to future technologies. “At the same time, our success in the ‘Academic Horizons’ funding scheme is a testament to Saarland University’s successful international focus on key technologies,” emphasises University President Professor Ludger Santen.

Under the project leadership of Professor Moritz Weber, the scientific director of the Centre for Quantum Technologies, the DAAD funding will be used to establish structures that will enable three key objectives: Firstly, the aim is to identify the best Master’s students and PhD candidates internationally in the fields of quantum technologies and artificial intelligence. Key tools in this regard are international partnerships and the establishment of international summer schools with research placements at the university. Secondly, these talented individuals will be supported with comprehensive assistance from initial contact right through to the start of their studies, in order to systematically promote their academic and social integration. And thirdly, these outstanding young researchers will be supported in their career development and retained in Germany as a research hub in the long term.

Jakob von Weizsäcker, Minister for Finance and Science of the Saarland, comments: “Advances in artificial intelligence and quantum technologies are driven by exceptionally talented and committed individuals. Attracting these talents to the Saarland is a key factor in our location’s appeal. The fact that Saarland University is among the universities selected nationwide is a mark of distinction for our research hub and underlines its international appeal.”

The Future Minds Programme Quantum AI thus makes a direct contribution to the DAAD’s ‘Academic Horizons’ programme. The aim of this programme is to identify and integrate outstanding international talent, to support their academic careers, and to strengthen the German innovation landscape in key technologies of the future – in this case, quantum technologies and AI – in the long term. The DAAD is funding the Quantum AI project from 1 May 2026 until the end of 2029 with a total of 709,000 euros.

Further information on the DAAD’s Academic Horizons programme:  https://www.daad.de/de/infos-services-fuer-hochschulen/weiterfuehrende-infos-zu-daad-foerderprogrammen/academichorizons/

For enquiries, please contact:
Prof. Dr Moritz Weber
Tel.: 0681 302-2556 
weber(at)math.uni-sb.de

From June 24 to 26, 2026, Saarbrücken was once again a hub for young computer science enthusiasts from all over Germany. Since 2008, the Saarland Informatics Campus, as the host of the “Saarbrücker Forschungstage”, has been offering selected high school students the opportunity to delve deeper into the world of computer science. The event is aimed at 14- to 20-year-olds who have distinguished themselves through exceptional achievements in the subject and are seeking an introduction to academic computer science. 

The goal of the Research Days is to inspire young talent to pursue a degree in computer science, to provide them with early exposure to research, and to support them on their path into academia.

This year, 42 young Germans participated, including 6 young women, as well as two students from a German school in Romania. As always, the invitees include the top participants from the second round of the 44th National Computer Science Competition (Bundeswettbewerb Informatik), as well as outstanding high school graduates who have particularly impressed in their advanced computer science and mathematics course.

The first meeting on the afternoon of June 24 was followed by a pre-event evening to set the tone and provide an opportunity for participants to get to know one another. This was followed by two days of exciting presentations, lectures, and workshops, each running from morning until late afternoon. On Thursday, an evening event titled “Ethics for Nerds” rounded out the research days thematically. The students could choose from 6 broader topics to create their own program; each participant attended three different workshops in small groups. The topics covered a wide range: from theoretical computer science and satellite communications to bioinformatics, game programming, and quantum computing as well as a session on AI-large language models. 

This year, the workshop instructors come from the Department of Computer Science of Saarland University, the Max Planck Institute for Informatics, the Max Planck Institute for Software Systems, the Center for Bioinformatics, and the CISPA Helmholtz Center for Information Security.

Participants also attended an advanced lecture program featuring presentations by Professors Thorsten Herfet (Saarland University) and Joël Ouaknine (MPI for Software Systems), who provided insights into their current research.

The Saarbrücken Computer Science Research Days are organized in close cooperation with the Nationwide Computer Science Competitions (Bundesweite Informatikwettbewerbe) and pursue the common goal of inspiring young people interested in computer science to engage with the subject. The German National Computer Science Competition has taken place annually since 1980 and is hosted by the German Informatics Society, the Fraunhofer ICT Group, and the Max Planck Institute for Informatics. The competition is supported by the Federal Ministry for Education, Family Affairs, Senior Citizens, Women and Youth.

Further Information:
Website of the Nationwide Computer Science Competitions: https://bwinf.de/

Editor:
Bertram Somieski
Tel.: +49.681.9302-5710
Email: somieski(at)mpi-klsb.mpg.de

The project addresses a fundamental challenge in modern cyber-physical systems and has far-reaching implications across a wide range of applications such as autonomous driving, robotics and space exploration, where safety and reliability are paramount.

The ERC-funded research will go beyond verifying that the theoretical design of control systems meets its intended specifications, such as driving at a certain speed or maintaining a certain distance from other vehicles. It will also ensure that these guarantees remain valid for the implementation of the systems, despite the timing variations that inevitably occur during execution.

Self-driving cars, increasingly sophisticated robots, and autonomous spacecraft such as autonomous spacecraft and planetary exploration vehicles are all examples of highly complex systems governed by advanced computer control. Their growing capabilities depend on ever more finely coordinated computations and increasingly precise timing. Yet timing remains a critical source of vulnerability. Even minor deviations in the synchronization of computational processes can propagate through a system and, in safety-critical applications, lead to serious or even catastrophic failures.

“Even the most advanced computer-controlled systems can be vulnerable to timing-related faults,” says Martina Maggio. “The Mars helicopter Ingenuity, for example, experienced dangerous oscillations caused by timestamp misalignments and later encountered another synchronization issue. These incidents demonstrate that timing errors are not rare edge cases but remain a fundamental challenge in the design and operation of complex autonomous systems.”

Despite these setbacks, Ingenuity ultimately became a remarkable success. During one incident, the helicopter was forced to perform an emergency landing after an image-processing task fell out of sync with other onboard computations. Maggio’s research focuses on preventing precisely such failures in software-controlled technical systems, from autonomous vehicles and industrial robots to spacecraft. This is particularly important in applications where correct timing is as important as correct functionality: if the timing of critical events cannot be guaranteed, the consequences can range from degraded performance to catastrophic system failure.

To advance this research at the highest international level, Martina Maggio has been awarded an Advanced Grant from the European Research Council (ERC). Since joining Saarland University as a Professor of Computer Science in 2020, Maggio has established herself as a leading expert at the intersection of control engineering and real-time systems. The ERC-funded project, entitled SCARF (Scalable CPS Analysis of Robustness to Failures), will begin in 2027 and run for five years.

Further information:

Prof. Dr Martina Maggio 

Email: maggio(at)cs.uni-saarland.de

Whether it is online banking, email, video streaming, global cloud platforms, or large-scale AI infrastructures – planetary-scale distributed systems form the backbone of many societal-scale applications. We take for granted the continuous availability of these services, even though outages can cause widespread disruption. Yet today, developers do not have principled approaches to provision, analyze, or prove performance or resilience properties of such systems. Currently, developers test for such properties using expensive but inadequate workload testing, and availability outages continue to be a problem for users.

The now EU-funded project “Pascal: Formal Performance Analysis at Scale” addresses the major challenge of formally reasoning about (that is, mathematically describing and verifying) the performance and resilience of large-scale distributed systems. Its ultimate goal is to develop methodologies and tools that system developers can use to reason about implementations of their systems.

Rupak Majumdar has been a Scientific Director at the Max Planck Institute for Software Systems since 2010 and an Honorary Professor in the Department of Computer Science at the Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU). This is the second ERC grant he has received. In 2015, together with Michael Backes, Peter Druschel, and Gerhard Weikum, he was awarded an ERC Synergy Grant for the project ImPACT: Privacy, Accountability, Compliance, and Trust in Tomorrow’s Internet. The ERC Synergy Grant is the European Research Council’s most highly funded grant scheme.

More info from the ERC.

Lenssen, who is currently a researcher at the Max Planck Institute for Informatics on the Saarbrücken campus, will further strengthen the Saarland Informatics Campus.

The ever-changing nature of the real world poses a challenge for artificial intelligence. Today’s AI systems are particularly well suited to text: they cope well with unambiguous, so-called ‘discrete’ data – that is, separate values that are countable and cannot grow continuously – as well as with symbolic information. In the real world, however, many things get out of their control. Here, much of the information is in continuous form: within a certain range, the values change constantly, such as sensor data or the movements of objects and people.

In his research, Jan Eric Lenssen explores how artificial intelligence can utilise and generate data that is present in so-called continuous representations – such as video, time series or three-dimensional sensor data. The Computer Science researcher develops methods of visual artificial intelligence that enable neural networks to process, analyse, model and generate such complex structures – a task that is significant for fields including robotics, physical and generative artificial intelligence, and image processing.

Jan Eric Lenssen became known, amongst other things, for PyTorch Geometric (PyG), a software library for graph-based neural networks, which he co-developed during his PhD at TU Dortmund University. PyG is now the world’s most widely used library of its kind and forms the technical basis for numerous research projects and practical applications. The start-up kumo.ai, based in Mountain View, California, which specialises in machine learning for relational databases and of which Lenssen was a founding member, also built upon this library.

At Saarland University, Lenssen is contributing his expertise in computer vision and artificial intelligence and aims to further expand his internationally successful research profile at the Saarland Informatics Campus. Through the joint research centre of the Max Planck Institute and Google, as well as a long-standing cooperation programme with Intel and the Saarland, his work is closely integrated with the international research landscape and industry.

Jan Eric Lenssen has received numerous awards for his research, including the DAGM German Pattern Recognition Award in 2025 and an Emmy Noether Fellowship worth around 1.9 million euros. His scientific work has also received high-profile accolades at leading conferences on computer vision and machine learning, including a Best Paper Award at ECCV 2022 and a Best Paper nomination at CVPR 2020. For his [doctoral] dissertation, he received the ECVA PhD Award and the TU Dortmund University Dissertation Prize.

Short biography 

Jan Eric Lenssen studied Computer Science at TU Dortmund University from 2009 to 2015, where he graduated with distinction. He subsequently obtained his doctoral degree there in 2022 with the highest distinction, ‘summa cum laude’. He undertook research stays at Meta Reality Labs (USA) and the AI company nnaisense (Switzerland), and from 2021 onwards was part of the founding team of the start-up kumo.ai. He then conducted research as a postdoctoral researcher and later as a senior researcher and research group leader at the Max Planck Institute for Informatics in Saarbrücken. Since 2026, Jan Eric Lenssen has been Professor of Computer Science at Saarland University and an associate member of the Max Planck Institute for Informatics.

For enquiries, please contact:

Prof. Dr Jan Eric Lenssen, email: jlenssen@mpi-inf.mpg.de 

Press photos available for download: 

Press photos can be found on this news webpage: https://www.uni-saarland.de/universitaet/aktuell/news.html

Derek Dreyer has set an exemplary standard of service within the PL community. Throughout the years, Derek has served in almost every possible role in our community, including General Chair of ICFP’19, Program Chair of POPL’24, and Associate Chair of OOPSLA’23 and PLDI’26. Since 2017, he is an Associate Editor of TOPLAS; since 2023, he is on the PACMPL advisory board; and since 2022, he is co-editor-in-chief of the Journal of Functional Programming, presently overseeing the transfer of the journal to Diamond Open Access. But Derek’s service work began much earlier. While still an assistant professor, he was moderator of the TYPES mailing list and volunteered to serve on the SIGPLAN Executive Committee (2012-15) as Awards Chair. In 2020, he helped safeguard the perception of programming languages research outside the field, when the CORE ranking committee threatened to demote the rankings of several major PL conferences. Derek has also been a prominent mentor for students, postdocs, and junior faculty. He chaired early editions of PLMW and co-founded the RTFM workshop on faculty mentoring (at PLDI’24 and POPL’26). He frequently speaks at such events, and is known for his widely-cited talks on speaking and writing skills: “How to Write Papers and Give Talks That People Can Follow”. Last but not least, Derek has written popular blog posts touching on the more “human” aspects of a career in research such as: accepting criticism, handling rejection, struggling to maintain a work-life balance, and impostor syndrome. In summary, Derek has served the community in many ways, always striving to promote the value and the quality of PL research, to help maintain cohesion in our community, and to empower junior researchers to conduct outstanding PL research.

The Distinguished Service Award is given by ACM SIGPLAN to recognize distinguished service contributions to the Programming Languages Community. The award recognizes contributions to ACM SIGPLAN, its conferences, publications, or its local activities. The award includes a prize of $2,500.

The report by the United Nations University (UNU) examines the environmental impacts of artificial intelligence, focusing primarily on generative AI models such as ChatGPT, Claude, and DeepSeek. Among other things, the study examines the resource requirements of data centers for training and operation, as well as the associated CO₂ emissions. As the use of generative AI increases, so does the computing capacity required for inference and training.

Prof. Maaß generally welcomes the debate but is critical of parts of the report. “The report makes a useful contribution by systematically compiling consumption figures for the first time and making them accessible to a broader audience,” he says. At the same time, he emphasizes: “The figures cited in the report are plausible in terms of their magnitude, but difficult to reproduce methodologically.”

Prof. Maaß generally welcomes the debate but is critical of parts of the report. “The report makes a useful contribution by systematically compiling consumption figures for the first time and making them accessible to a broader audience,” he says. At the same time, he emphasizes: “The figures cited in the report are plausible in terms of their magnitude, but difficult to reproduce methodologically.”

This is precisely where the ESCADE project, funded by the Federal Ministry for Economic Affairs and Climate Action, comes in. As part of this project, DFKI is researching energy- and cost-efficient approaches for operating AI applications in data centers. Using the EAVE demonstrator, energy consumption, CO₂ emissions, and operating costs of different model, hardware, and location configurations are made comparable.

In doing so, ESCADE addresses key transparency and decision-making issues that are also relevant in the context of the UN report. The scientific foundations and design principles were described, among other places, in a CAiSE publication on energy- and cost-efficient AI operations. In this way, the project helps not only to discuss the environmental impacts of AI but also to make them measurable and practically comparable.

“The energy consumption of AI data centers is real and growing, but it is not currently the dominant climate issue,” says Maaß. It is therefore crucial to consider technical development, site selection, and regulatory frameworks collectively. Against this backdrop, the question of how AI systems can be designed in the future so that their benefits do not come at the expense of unnecessarily high resource consumption is gaining importance. 

The UNU-INWEH report:https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints

Statements on the UN report:https://www.sciencemediacenter.de/angebote/un-bericht-zum-umweltfussabdruck-von-ki-und-rechenzentren-26129

Paper „Towards Decision Support Systems for Cost-Effective and Energy-Efficient AI Operations in Data Centers“: https://link.springer.com/chapter/10.1007/978-3-032-28110-4_22 

ESCADE project webpage: https://www.dfki.de/web/forschung/projekte-publikationen/projekt/escade

The formal signing ceremony took place in the presence of Dorothee Bär, German Federal Minister of Research, Technology and Space, and Philippe Baptiste, French Minister for Higher Education, Research and Space. The aim of the ambitious project is to establish a powerful European AI player at the intersection of cutting-edge research, industry, and society.

The new binational center bundles the excellent research capacities of both countries to provide viable answers to the rapid global AI transformation. In the face of intense international competition, the center makes a decisive strategic contribution to Europe’s digital sovereignty. Through a permanent, institutionalized structure and the close integration of science and industry, the development of trustworthy, transparent and competitive AI technologies “Made in Europe” is to be accelerated.
 

“The founding of the open, French-German Center marks a decisive milestone for the future of European AI,” explains Prof. Dr. Antonio Krüger, CEO of DFKI. “Through the concrete integration of the excellent AI research of Germany and France, we are moving beyond mere declarations of intent to actively create a foundation for Europe’s digital sovereignty in artificial intelligence.”

Dr. Bruno Sportisse, Chairman and CEO of Inria, adds: “This Franco-German Center is one answer to the AI challenge. We have to align our strategies and to deliver impactful projects if we want to play the race at the forefront of research, technology and innovation. This requires to have trustworthy partnerships, a joint long-term roadmap and the ability to leverage with our ecosystems (AI clusters): this is what we build with DFKI.”

The integrated “Project Factory” at the core of the Center functions as an agile innovation engine to flexibly transform bilateral research and development projects into tangible prototypes and software solutions. Furthermore, the explicitly open concept of the new Center promotes the active participation of other academic partners from both countries.

Furthermore, companies from both countries will receive direct access to projects in order to develop innovative AI applications in key sectors such as Industry 4.0, health, and mobility, and to bring them swiftly to market maturity. This research transfer is flanked by talent promotion and researcher mobility programs, which turns the Center into a highly attractive magnet for top global talent through bilateral career paths and joint summer schools. The profile is complemented by the establishment of a scientific think tank that develops well-founded recommendations for action on the social impact of AI, strengthens AI literacy among the population, and advises stakeholders from politics and business on issues regarding regulation, ethics, and standards.
 

The formal signing within the framework of Vivatech underlines the high political priority of cooperation for both governments.
 

Dorothee Bär, Federal Minister of Research, Technology and Space, declares on the occasion of the ceremony: “In today’s world, artificial intelligence (AI) is a decisive competitive factor. Hence, it was named as a key technology in the High-Tech Agenda Germany. We have to prevent Europe from being sidelined in the global AI landscape. The binational French-German Center for AI as collaboration between DFKI and Inria will help to seize emerging opportunities and leverage the combined expertise of France and Germany to drive Europe forward. It will conduct joint research on secure and trustworthy artificial intelligence, fully aligned with European standards. This partnership exemplifies how close Franco-German cooperation can shape a stronger, more innovative European AI ecosystem.”

The binational Center is deliberately not designed as a closed system. The integrated Project Factory is open to further AI research clusters, universities and scientific organizations from Germany, France and beyond, as well as partners from industry and public administration, to weave a seamless European AI network.

Following the formal signing on  June 18, the step-by-step establishment of the structures as well as the setting up of dedicated offices at the respective locations of DFKI and Inria in Germany and France will begin from July 2026, followed by the launch of the first operational programs in late 2026.

Further information can be found at: https://french-german-ai-center.org

Soofi S marks the beginning of the project’s first release phase. The model is designed for organizations that need to run AI applications transparently, adaptively, and on their own or sovereign infrastructure — for example, in industrial processes, the analysis of extensive technical and regulatory documents, code generation, or agentic AI systems. Trained from scratch on 27 trillion (27T) tokens, Soofi S is a 30 billion parameter (30B-A3B) mixture-of-experts model whose hybrid Mamba-Transformer architecture combines high throughput with low energy consumption. Soofi S has been trained primarily on English and German text and achieves top-tier results among open models in its size class in English; in German, it leads the peer group. Soofi S is initially released as a base model, which can already be fine-tuned for specific domains; post-trained variants for dialogue and agentic applications will follow.

“Whoever controls the foundation models controls a central part of future digital value creation and strengthens their sovereignty and resilience. With Soofi, we are building an open foundation on which businesses, SMEs, and the public sector can develop transparent AI applications based on their own data, without becoming permanently dependent on individual non-European models,” says Jörg Bienert, Managing Director of the Center for Sovereign AI, German AI Association.

A particular focus lies on transparency: the consortium will not only release model weights but also publish technical documentation on training methodology, data preparation, and the data pipelines used. This makes Soofi S more auditable for businesses, public authorities, and researchers, and more readily adaptable to specific use cases.

Soofi S and subsequent models are trained on Deutsche Telekom’s Industrial AI Cloud in Munich, using NVIDIA’s open-source AI framework. Initial results show that the Soofi S base model matches or outperforms international models of comparable size across German and English benchmarks.

“Soofi S is not intended as yet another general-purpose chatbot, but as a technical foundation for industrial AI. What matters is that Soofi S performs not only well in benchmarks, but can be deployed reliably, efficiently, and transparently in production,” says Nicolas Flores-Herr, Technical Project Lead for Soofi and Team Lead at Fraunhofer IAIS.

“Digital sovereignty arises where cutting-edge scientific research and industrial practice intersect directly. With ‘Soofi S,’ we are demonstrating that Europe holds the keys to the next generation of AI technologies. The DFKI is contributing its expertise here to develop models that combine performance with transparency. This is a crucial lever for providing the European economy with an independent and future-proof AI infrastructure.”

Prof. Dr. Antonio Krüger, CEO DFKI

At PLDI this year, only 10 papers were given this award out of 115 accepted papers.

DFKI and Inria have been collaborating in a bilateral partnership since 2020. The focus is on joint research projects carried out by mixed teams from both organizations. Two current projects exemplify societal challenges in Germany and France and make a concrete contribution to greater social participation. The “RoGSiLT” and “NEARBY” projects will be presented at the joint booth in the German Park.

RoGSiLT (Robust and Generalizable Sign Language Translation) is developing AI-based solutions for German and French Sign Language (DGS and LSF). Innovative AI methods aim to significantly improve translations between spoken language and sign language. The goal is to improve both the translation of text into sign language and the conversion of sign language from videos into written language. Modern techniques such as multimodal neural networks, self-supervised learning, and large language models are intended to overcome existing hurdles—such as limited data availability, lack of generalizability, and unnatural translations. A key component is the development of new data resources, including extensive parallel corpora of sign language videos and associated texts.

Project presentation: Wednesday, June 17, all day, Hall 7.3, Booth 3E14

Brain-computer interfaces (BCIs) open new avenues for human-machine interaction by using brain signals to directly control technical systems. NEARBY (Noise and variability-free BCI systems for out-of-the-lab use) develops innovative BCI systems with low noise and variability that function reliably even outside the laboratory. The goal is to create robust, practical solutions that pave the way for the use of brain-computer interfaces in everyday life—for greater self-determination, efficiency, and intuitive interaction. At the conclusion of the project, the German-French team will present the current state of research.

Project presentation: Thursday and Friday, June 18–19, all day, Hall 7.3, Booth 3E14
 

DFKI and Inria are taking their partnership to a new level: The leading research institutions will sign an agreement to establish an open, binational German-French research center for artificial intelligence.

The signing ceremony will be held in the presence of Dorothee Bär, Federal Minister for Research, Technology, and Space, and Philippe Baptiste, French Minister for Higher Education, Research, and Space. This ambitious project aims to establish a strong European AI player at the intersection of cutting-edge research, industry, and society.

German Park Stage, June 18, 10:00–10:30 a.m.

www.vivatech.com
https://www.dfki.de/en/web/qualifications-networks/international-cooperation/german-french

https://french-german-ai-center.org
 

Graduates with a Bachelor’s degree in physics, mathematics or computer science can apply for a place on the programme until 30 June.

Quantum computing is widely seen as one of the key technologies of the 21st century. The global development of powerful quantum hardware systems continues to gather pace and with it grows the demand for new algorithms, software concepts and cryptographic methods. And this is where the new Master’s programme comes in. With its focus firmly on the software side of quantum computing, it deals with the theoretical foundations of information processing in quantum systems. ‘Quantum information theory is, in a sense, the theoretical underpinning of the software used in quantum computers. It is rooted equally within the fields of mathematics, computer science and physics, and calls for an integrated and interdisciplinary perspective,’ explains Professor Moritz Weber, Scientific Director at the Center for Quantum Technologies (QuTe) at Saarland University, who has been instrumental in designing the new degree programme.

Compared with other study programmes in the quantum technologies sector, many of which are strongly physics-focused, the Saarbrücken Master’s programme is based at the Department of Mathematics and focuses particularly on mathematical and computer science aspects. The M.Sc. programme in Saarbrücken therefore offers a route into the world of quantum research even for students with no prior background in physics. ‘At its core, the programme addresses the fundamental questions raised by the development of quantum computing technology: How do you actually program a quantum computer? In what way does it function differently from a classical computer? What characterizes quantum computing and what does that have to do with mathematics,’ says Moritz Weber. Students on the programme acquire a solid grounding in areas such as quantum algorithms and the mathematical foundations of quantum information. Mandatory elective modules allow students to specialize in areas such as quantum complexity, quantum error correction or other research fields of current interest.

A particularly attractive feature of the Master’s programme is that students gain early exposure to the questions of current research relevance. This is possible because of the close links that exist between the study programme and the recently established Center for Quantum Technologies, and the collaborative ties with other leading research institutes in Germany, including the Helmholtz Center ‘Forschungszentrum Jülich’. Students can also gain practical experience through a supervised internship – either in a research or industry setting. The programme also offers flexible study pathways, enabling students to combine quantum computing equally with either classical computer science or mathematics. This flexibility allows them to tailor their studies to their individual interests and longer-term career aims.

Graduates from the programme will be highly sought-after specialists in a very innovative and fast-growing sector. Career opportunities range from research and development to the quantum computing industry and the IT and cybersecurity sectors, for example in post-quantum cryptography. Further prospects include professional roles in software development, data analysis and high-performance computing. At the same time, the programme provides targeted preparation for an academic career, including doctoral study in the broader field of quantum science.

The Master’s programme is taught entirely in English and has been intentionally designed to appeal to international students as well. The standard period of study is four semesters, and the programme leads to a Master of Science degree. The deadline for applications for the coming winter semester is 30 June.

Further information and details about how to apply on this Website.

Thanks to €48 million in support from European and national funding sources, the RICAIP (Research and Innovation Centre on Advanced Industrial Production) project has created an interconnected ecosystem of industrial testbeds at CIIRC CTU and CEITEC BUT in Brno, linked with leading German research centres DFKI and ZeMA, as well as additional partner institutions across Europe. The testbeds enable systematic development and testing of new approaches in industrial AI, robotics, distributed manufacturing, and advanced automation under conditions close to real-world industrial operations, significantly strengthening the ability to transfer research results into industrial practice.

“RICAIP is a truly European effort. At the intersection of education, research, innovation and industrial development, we have built a technological infrastructure and a network of teams in the Czech Republic and German that can be a leader in the future of Industry 4.0. We have set up the RICAIP testbeds and filled them with an ecosystem of collaboration with many stakeholders. At the same time, it is an ongoing process of setting up a sustainable format as the RICAIP Centre and continuing to expand European cooperation.”

Dr. Tilman Becker, RICAIP Director

The conference opened a broader discussion on how intelligent physical systems, autonomous robotics, and AI-driven manufacturing will shape the future of European industry. Keynote speakers included Prof. Wolfgang Wahlster from DFKI, one of the founding figures of Industry 4.0, and Prof. Duncan McFarlane from the University of Cambridge, a leading expert in industrial intelligence systems and digital twins. The programme also featured Valentina Ivanova, Deputy Director for European and International Affairs at CEA-List and coordinator of the European AI-MATTERS initiative focused on testing and experimentation facilities for AI in manufacturing. Their contributions provided a forward-looking perspective on industrial AI, robotics, and intelligent physical systems in a European context.

The interaction between artificial intelligence and the physical world is one of the key scientific and technological challenges of our time, with a profound impact on the future of manufacturing, logistics, and other industrial sectors,” said Prof. Vladimír Mařík, Scientific Director of CIIRC CTU and principal investigator of the RICAIP project. “RICAIP has created a strong environment for the development of the Czech AI ecosystem and has significantly contributed to new initiatives such as the Czech AI Factory“.

According to Prof. Mařík, close cooperation between Czech institutions and German partners DFKI and ZeMA has played a crucial role. “Thanks to this collaboration, CIIRC CTU in Prague and CEITEC BUT in Brno are now recognised as respected European centres of excellence in industrial AI, also involving VSB – Technical University of Ostrava. At the same time, we are working intensively to ensure the long-term sustainability of both the infrastructure and research potential, as European industry will require strong scientific and technological support during the ongoing digital transformation.” 

“Collaboration initiated within RICAIP contributes to European digital sovereignty in the field of industrial artificial intelligence. Close cooperation and the shared use of state-of-the-art test environments have created a European innovation ecosystem that enables us to independently develop, scale, and deploy key technologies in industrial AI.”

Prof. Antonio Krüger, CEO DFKI

The importance of interconnected testbeds was also highlighted by representatives of individual sites. “The future of industrial AI is not created in isolated laboratories, but in interconnected research infrastructures where technologies, expertise, and experiments can be shared across countries,” said Khansa Rekik from ZeMA. “Within RICAIP, we were able to connect locally developed robotic and AI solutions into broader distributed manufacturing scenarios.”

“RICAIP represented a major qualitative leap for our testbed,” added Dr. Pavel Burget, Director of the RICAIP Testbed Prague. “We have created an environment where cutting-edge research meets real industrial applications – from autonomous robotic manipulation and digital twins to AI-based quality inspection in extremely short production cycles.” 

“Long-term international cooperation and shared research infrastructures are key to developing future technologies. For our institute, participation in such initiatives is an opportunity not only to advance research, but also to transfer its results into industrial practice and strengthen European competitiveness in AI and advanced manufacturing,” confirmed Prof. Radimír Vrba, Director of CEITEC BUT in Brno.

The importance of cooperation between research and industry was also emphasised by representatives of the industrial sector. “Strengthening Europe’s competitiveness requires much closer cooperation between research and industry. Initiatives such as collaboration between companies, CIIRC CTU, and infrastructures like RICAIP clearly demonstrate how research results can be transformed into real industrial value. Digitalization, automation, and artificial intelligence will be the key drivers of transformation in European industry in the coming decade. At the same time, we need an environment that motivates companies to invest in research and innovation,” said Martin Jahn, Member of the Board for Sales and Marketing at Škoda Auto, Vice-President of the Confederation of Industry of the Czech Republic, and President of AutoSAP.

“If Europe is to remain competitive on the global stage, we cannot innovate in isolation,” added Eduard Palíšek, CEO of Siemens Czech Republic. “Close collaboration between industry and academia, such as that represented by CIIRC CTU and the RICAIP testbeds, allows us not only to validate new technologies in real industrial environments, but also to jointly address challenges such as manufacturing resilience and cybersecurity. True competitiveness is not built on new technologies alone, but also on the courage to share know-how and push the boundaries of what digitalization makes possible.“ 

RICAIP Days 2026 thus represented not only the conclusion of a successful European project, but above all the beginning of a new phase of European cooperation in industrial AI, intelligent physical systems, and technology transfer between research and industry.

The conference was followed by Tech Dating 2026, an open day at CIIRC CTU for companies, organised in cooperation with EDIH CTU, AI-MATTERS, the National Centre for Industry 4.0, CzechInvest, and other partners. It offered hands-on consultations, technology demonstrations, and the opportunity to discuss concrete challenges in AI, automation, and digitalisation directly with research teams from CIIRC CTU and partner institutions.

The Fonds der Chemischen Industrie (FCI) is funding the project led by Professors Tanja Gulder and Andrea Volkamer to the tune of €80,000 as part of a special funding programme designed to make data science a permanent part of chemistry degree programmes. 

The following text has been machine translated from the German with no human editing.

Whether substances are being analysed, experiments conducted, new active compounds tested or theoretical models examined: everything that takes place in a chemistry laboratory generates data. Without these measurements and observations, nothing works – if they are not recorded, the results of even the best work are lost. What has been true since the dawn of Chemistry is even more so today. “Data and its processing are an indispensable foundation for research and development. New computer-aided methods, including artificial intelligence, are creating new ways to achieve research results using large amounts of data and to make them usable for industry and society,” explains Tanja Gulder, Professor of Organic Chemistry at Saarland University. She researches and develops sustainable chemical processes modelled on nature, including for novel and improved active substances, using digital computer methods. 
“Technology is significantly transforming the chemistry and pharmaceutical industries. This is also changing the demands placed on the specialists working in these fields. Industry is looking for chemists who possess knowledge of data, its machine- -based processing and the use of AI,” says Tanja Gulder. Chemistry education must catch up and integrate digital methods into teaching from the very start. “So far, the Chemistry curriculum in Germany has not covered this area sufficiently. Even some PhD students today are reluctant to work with data. That is why we want to introduce digital content into Chemistry degree programmes using a new teaching concept, starting with the Bachelor’s degree and continuing throughout to the Master’s degree,” explains Gulder. 

To this end, the chemist at Saarland University is collaborating with Andrea Volkamer, Professor of Data-Driven Drug Design. The computational chemist develops computer-based methods—encompassing both algorithmic approaches and AI models—to predict which drug candidates are the most promising; she is also a specialist in digital teaching methods. “Knowledge of the use of artificial intelligence needs to be better integrated into the curriculum in general,” emphasises Andrea Volkamer.

The two researchers are now receiving funding for their project under the special funding programme of the Chemical Industry Fund (FCI): the Saarbrücken application was successful in a nationwide competition alongside 22 other universities and colleges. Numerous institutions had submitted applications for funding. The Saarbrücken project concept will receive 80,000 euros in funding over three years. In total, the fund is investing 1.6 million euros in data science within Chemistry degree programmes to embed innovative teaching concepts on AI, big data and laboratory automation in higher education.

Over the next three years, Gulder and Volkamer will develop a teaching concept with their teams. “The concept is intended to serve as a sustainable model,” says Andrea Volkamer. Its modules will initially be developed within the Department of Organic Chemistry at Saarland University, but they are intended to be applicable beyond the boundaries of this discipline – generally across the life sciences, such as in Pharmaceutical Science or biotechnology – and beyond the Saarbrücken campus to other universities. 

The new concept for the introductory bachelor’s practical course aims to teach students the basics of working with data and data management – in other words, how to generate data as the foundation for machine learning and how to store it in a way that is both useful and retrievable by third parties. “We also want to teach students the ‘FAIR’ principles of research data management,” explains Tanja Gulder. FAIR stands for the initial letters of the English terms ‘findable’, ‘accessible’, ‘interoperable’ and ‘reusable’. “The advanced practical course will, among other things, focus on introducing students to working with databases such as electronic lab notebooks and providing them with a basic understanding of AI, machine learning and programming languages. The aim is to equip them with the tools to analyse large volumes of data, identify complex patterns and correlations, optimise synthesis routes or predict reaction outcomes,” explains the chemist.

Gulder, who is involved in two major collaborative research centres and two Research Training Groups run by the German Research Foundation (DFG), is working alongside colleagues at the University of Leipzig to set up another major research project in the field of digital Chemistry: the aim here will be to advance chemical research for drug discovery using modern computer-aided methods, including artificial intelligence. “With this new teaching concept, we are also training the next generation of chemists who will be working on this research project,” says Gulder, explaining her long-term goals.

The Chemical Industry Fund was established in 1950 and is the funding body of the German Chemical Industry Association. The Faculty of Natural Sciences and Technology and the Chemistry department at Saarland University are contributing an additional 20 per cent of the funding as an investment in the quality of teaching.

For further information:

Prof. Dr Tanja Gulder: Email: tanja.gulder@uni-saarland.de

Prof. Dr Andrea Volkamer: Email: volkamer@cs.uni-saarland.de

Press photos available for download:
Press photos can be found on this news website: 

https://www.uni-saarland.de/aktuell/ki-im-Chemistry-studium-47017.html
You may use the press photos free of charge in connection with this press release and reporting on Saarland University, provided you credit the photographer.

‘Software solutions are embedded in our everyday lives, and when they are faulty the consequences can be serious. So, it‘s essential that programmers understand their code and don‘t overlook errors or introduce new ones when adding further functionality,’ says Sven Apel, professor of computer science at Saarland University. Apel and his colleagues want to understand more precisely what happens in a software developer’s brain when writing and analysing code. Three years ago, he brought Axel Mecklinger, professor of experimental neuropsychology at Saarland University, into the project. Together, they combined electroencephalography (EEG) with eye-tracking data to record signals known technically as fixation-related potentials (FRPs). ‘The advantage of the FRP approach is that it allows us to record brain activity at the exact moment when the eyes stop moving and focus on a specific target,’ explains Axel Mecklinger.

The research team set out to discover how software developers react when they encounter confusing snippets of code known as ‘atoms of confusion’. These small portions of code occur quite frequently in source code and cause a person and a machine to come to different conclusions regarding the output. While the computer can interpret and execute them unambiguously, they are not intuitively clear to the programmer, which means the programmer may misunderstand how the program works. Anna-Maria Maurer, a doctoral research student in computer science working with Professor Apel, incorporated this type of confusing code into the experimental design. She recruited 24 programmers as participants, whose brain activity and eye movements were recorded over around 1,700 trials.

To analyse the measurement data, the team drew on methods and expertise from psycholinguistics, although the methodology could not simply be transferred to the study of software programming. Earlier studies had already shown that programming activates brain regions similar to those involved in natural language processing, but the way programmers approach code differs from the way people process language. ‘When we want to understand how the brain processes particular conversational situations, we ask participants to read short text passages and compare this with the EEG and eye-tracking data. But when reading code, programmers process larger contextual blocks, scanning several lines at once and perceiving complex structures as single units,’ explains Vera Demberg, professor of computational linguistics, who with her team was involved in analysing the data. To take this added complexity into account, the experimental set-up and design had to be correspondingly more sophisticated. The code snippets were presented to the participants in three thematic blocks, with each block comprising 24 individual trials, while EEG and eye-movement data were recorded and synchronized to the millisecond.

When the team compared EEG signals from earlier natural language studies with the new findings from their software programming study, they identified a striking pattern known in neuropsychology as late frontal positivity. ‘When the programmers encountered confusing snippets of code, they showed brain activity similar to that seen in linguistic experiments where participants read sentences containing unexpected turns of phrase. The brain then adapts in a split second, checking the information against long-term memory and updating the mental representation of the new situation in order to make sense of it,’ explains Vera Demberg. To illustrate what’s happening, Axel Mecklinger cites the sentence ‘Theo wants to chop wood, so he goes to fetch a jacket.’ as an example of just such an unexpected turn in conversation. Upon encountering the words ‘goes to fetch’ the reader would normally expect this to be followed by ‘an axe’. A jacket, by contrast, is certainly plausible, but still comes as a surprise in this context. ‘In our EEG experiments on language processing, unexpected words such as “jacket” generate a late frontal positivity – a signal that bears a very strong resemblance to the EEG response elicited by confusing code snippets,’ says neuropsychologist Axel Mecklinger.

‘Because programmers spend 70 to 80 percent of their time trying to understand code, it’s important that we understand how their thought processes work. The insights we gain can help us develop better tools that either eliminate coding pitfalls from the outset or make them easier to detect. These findings could also inform how we go about training software developers,’ explains computer scientist Sven Apel. In future studies, he hopes to investigate whether programmers show different patterns of brain activity when the confusing snippets of code are actually faulty, or when they are shown lines of code that do not require spontaneous rethinking, i.e. spontaneous revision of the programmer’s mental representation of the situation.

The study, published in the prestigious journal Scientific Reports, involved Annabelle Bergum, Anna-Maria Maurer, Norman Peitek, Regine Bader, Axel Mecklinger, Janet Siegmund, Vera Demberg and Sven Apel. All of the authors are researchers at Saarland University, with the exception of Janet Siegmund, who is professor of software engineering at Chemnitz University of Technology. The study is linked to several major research programmes at Saarland University and received funding from them. These include the Transregional Collaborative Research Centre 248, ‘Foundations of Perspicuous Software Systems’ (co-spokesperson: Professor Holger Hermanns), the ERC Advanced Grant ‘Brains on Code’ (PI: Professor Sven Apel), and the Collaborative Research Centre 1102 on Information Density and Linguistic Encoding (spokesperson: Professor Elke Teich), in which Regine Bader, Vera Demberg and Axel Mecklinger are involved.

Original publication:
Annabelle Bergum, Anna-Maria Maurer, Norman Peitek, Regine Bader, Axel Mecklinger, Vera Demberg, Janet Siegmund and Sven Apel, Fixation-related potentials reveal that confusing program code elicits a late frontal positivity. In: Scientific Reports 16, 16833 (2026): https://doi.org/10.1038/s41598-026-50946-9 

Further information:
Chair of Software Engineering: https://www.se.cs.uni-saarland.de 

Questions can be addressed to:
Professor Sven Apel
Chair of Software Engineering
Saarland University
Tel.: +49 681 302-57211
Email: apel(at)cs.uni-saarland.de