Six Vidi grants for UT researchers

Dr. M. Carioni

It is becoming clear that our society is overwhelmed by a growing flood of information, much of which is either redundant or unreliable. Even in our daily lives, we constantly face the challenge of filtering content from a variety of media. One way to tackle this challenge is through compression methods, which are already widely applied to finite-dimensional data. However, when it comes to infinite-dimensional structures which are often used to describe modern data, such methods remain unexplored. SPARGO aims to bridge this gap by developing a new framework for infinite-dimensional compression.

Dr. ir. A. Continella

The software holding our digital society is vulnerable to attacks from cybercriminals. Researchers and companies rely on techniques to discover vulnerabilities in production software and mitigate them. However, current tools detect too many potential flaws, overwhelming organisations, and do not provide actionable insights, leaving services still highly vulnerable. In this project, the researchers design and develop automated techniques to analyse discovered vulnerabilities and assess their risk in the context of specific IT infrastructures, prioritising the critical ones. Unlike prior work, the researchers analyse infrastructures as a whole, instead of individual applications, allowing for prompt, effective mitigation and reducing costs.

Prof. Dr. G. Gantner

Partial differential equations are used to mathematically model numerous changing-in-time processes, e.g., conduction of heat, motion of fluids, propagation of acoustic/electromagnetic waves, and many others. These equations are rarely solvable exactly. Therefore, numerical algorithms are indispensable tools to approximate their solutions with the help of computers. Specifically for heat conduction processes, this research aims to develop and mathematically analyse novel algorithms that accurately approximate the exact unknown temperature at minimal computational cost, so that even complex problems can be solved within realistic time and resource constraints.

Dr. L.I.M. Lenferink

Nine per cent of children (aged <18) face the death of a parent or sibling. Grief research is focused on individuals, while multiple people are affected by the same loss.
This research in 250 parent-child (aged 12-20) pairs whose household member died to what extent:

1)       Grief is transmissible between parents and children.
2)       Grief transmission takes place at multiple timescales, such as hours, days, weeks, and months, that vary in strength and direction between timescales.
3)       Family dynamics are key to grief transmission.

Collecting survey data and audio recordings via smartphones provides first insights into grief transmission and improves bereavement care.

Dr. G. Rattan

Learning from Complex Systems: The Mathematical Foundations makes a chemical molecule a good medicine? How fast can ideas spread through social networks? Why are certain complex systems, such as the human brain or the internet, so hard to understand? The emerging field of Graph Learning promises to unravel the hidden patterns inside complex systems. At the heart of this approach are Graph Neural Networks (GNNs): They are AImodels that can reason about relationships, not just data. We establish the mathematical foundations for next-generation GNNs that are smarter, faster, andmore trustworthy.

Dr. N. Strisciuglio

Current AI advancements are enabled by vast datasets for training, yet these datasets often contain biases that lead to unreliable and unfair outcomes. Additionally, AI development is dominated by a few large companies with access to massive data resources. In this project, I will develop data-efficient AI by leveraging prior knowledge and visual compositionality to reduce dependency on large datasets and the impact of biases. By enabling AI to learn effectively with less data, I aim to create more accessible and reliable models, challenging the current paradigm and democratizing AI development.