In the Netherlands, approximately 1 million children (0-25 years) have a chronic disease. Above and beyond the ever-present challenges of growing up with an illness, these children have 40% chance to develop psychological problems, including depression, anxiety and loneliness. Throughout their life, this translates into decreased well-being and reduced social participation and generates additional costs for society. Early prevention of psychological problems is thus key to break this vicious cycle. Therefore, eHealth applications are promising. However, scientific knowledge is missing and validated tools are not yet available for this group and involved health care professionals. Our mission is to make scientifically validated eHealth tools that allow personalized and trans-diagnostic prevention of psychological problems widely available for this highly vulnerable group of chronically ill children and future adults, through an accessible, user-friendly, safe, and sustainable platform. To succeed in this mission, we present an iterative learning cycle approach in two four-year phases during which we gather the insights, and develop, evaluate, and implement the much needed eHealth tools: I. Development: Distil and validate the theoretical and game-design factors that make eHealth effective for chronically ill children. II. Evaluation: Evaluate trans-diagnostic and personalized eHealth tools for chronically ill children, using and developing state-of-the-art methods. III. Implementation: Study and remove the barriers that currently hinder implementation and uptake, and threaten availability of eHealth applications for chronically ill children. Our eHealth junior consortium includes (applied) researchers, pediatricians, psychiatrists, psychologists, patient organizations, knowledge centers, game designers, industrial designers, insurance companies, and business professionals. We will collaborate with the end-users (children, families, and professionals) in order to achieve both international scientific breakthroughs and optimal clinical and societal impact. Knowledge utilization is a crucial part of our project.

In order to secure a sustainable future, it is crucial that consumers adopt more resource-efficient foods, such as soy butter, cultured beef and seaweed. One obvious marketing strategy is to position sustainable alternatives as the morally-superior choice. The main selling point of cultured beef, for instance, is its contribution to the collective good. But while such moral appeals could indeed boost demand, this proposal examines the notion that there are some important, previously unconsidered, social risks associated with moral appeals. Specifically, while compliance with moral appeals could satisfy consumers? internal need to be moral, it also calls the morality of fellow consumers into question. In response, fellow consumers may attempt to restore their threatened sense of morality by discrediting or rejecting moral outliers ? a phenomenon known as ?moral do-gooder derogation?. This social-psychological perspective on morality may have some important implications for the marketing of sustainable products, which I will examine in a series of lab and field studies. Project 1 takes the perspective of the observer. It documents defensive responses by consumers who witness others buying ?morally-superior? sustainable products. Importantly, I additionally propose that prospective buyers also anticipate such defensive responses. Project 2 therefore takes the perspective of the actor. It investigates the notion that consumers may avoid purchasing ?morally-superior? sustainable products in an attempt to avoid being perceived as a moral outlier. In doing so, this proposal introduces a new angle on the adoption of sustainable products: consumers want to be moral, but not be perceived as a moral outlier.

In theoretical computer science, we study algorithmic problems and their various levels of difficulty. The mostfamous level of difficulty is NP‐completeness. This proposal deals with algorithmic problems that are believedto be even more difficult than NP‐complete problems: the class of ER‐complete algorithmic problems. The diffi‐culty of ER‐complete problems stems from the inherent presence of continuous numbers in the solution. Manyprofound and important algorithmic problems are ER‐complete. The art gallery problem, geometric packing,real‐root‐finding, Nash‐equilibria, polytope realizability, and training neural networks are prominent examples.Methods to find solutions to those algorithmic problems generally fall into three categories: Algebraic methods,gradient descent, and discretization schemes. Algebraic methods are mathematically guaranteed to always re‐turn the optimal solution, but in practice take an infeasible amount of time. Gradient descent often convergesvery fast in experiments but has no guarantee on the quality of the solution or the time it takes to converge.This discrepancy between theoretical and practical methods forms an abyss in our understanding. Discretizationschemes have the potential to bridge this abyss. They are much faster than algebraic methods. Moreover, (usingadditional assumptions) they have the potential to provably terminate in finite amount of time. As a proof ofconcept, I developed a new discretization scheme for the art gallery problem. The discretization scheme is fasterthan any previously fully published algorithm on benchmark instances and it returns reliable solutions in prov‐ably finite time. I will develop new powerful discretization schemes for other central ER‐complete algorithmicproblems in this proposal. Furthermore, I will strengthen our theoretical understanding of ER‐completeness andbroaden its scope even further.

Attention-Deficit/Hyperactivity Disorder (ADHD) affects about 8% of children and often continues into adulthood. While many symptoms of ADHD are observable, its assessment frequently relies on subjective reports, leading to potential inconsistencies and biases. I aim to develop the first automated system for assessing ADHD in children through video recordings of parent-child interactions. By integrating advanced machine learning techniques to analyze visual, vocal, and verbal behaviors, I will provide an interpretable evaluation. This approach will explore gender-related differences in key behaviors and enhancing the understanding and assessment of ADHD while laying groundwork for future applications in evaluating other neurodevelopmental disorders.

Network science is in great need of algorithms that can analyze increasingly larger networks fast. However, this ambition is undermined by the recent theory of fine-grained complexity. It predicts tight (conditional) lower bounds on the complexity of graph distance, counting, and enumeration problems that underlie network science. These lower bounds, while polynomial, are too high for the staggering size of modern data sets. Fortunately, the lower bounds may be circumvented by parameterized algorithms. Still, we lack systematic studies into the effectiveness of this approach. In particular, commonly studied parameters are linear in the input size for standard models of networks. Hence, current parameterized algorithms might not yield the urgently needed improvements to analyze current and future real-world networks. The proposal aims to design new parameterized algorithms to enable the analysis of huge networks. The proposal will initiate a design cycle to discover, analyze, exploit, and validate new parameters geared towards the graphs and problems commonly encountered in network science. This will be achieved through interaction with domain experts, the analysis of data and mathematical models, and building the required algorithmic knowledge of parameterized computation within P. The project will yield a parameter ecology for polynomial-time problems and implementations of the discovered algorithms. In doing so, the proposal seamlessly integrates fundamental research and practical considerations, and may impact the many application areas of network science.