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.

TeamMeUp is a computational team formation solution for the video games industry. Video game development is a creative sector with significant economic impact, which depends on the efficient collaboration of creative professionals from various backgrounds to develop the next successful video game. However, bringing together an efficient team of these professionals is challenging, leading to significant economic and social repercussions for the games industry. Based on optimization algorithms and social sciences, TeamMeUp helps address the games industry’s inefficient talent coordination problem. In this project, we will systematically assess the technical and commercial feasibility potential of our solution.

In this project, I will develop a research-based toolbox to promote social safety in multi-player online games. Competitive games in particular are known to be breeding grounds for discrimination and hate speech. The only effective tools currently available to game developers are band-aid solutions, such as features to report, block or remove abusive players. A real solution requires new strategies so that players can feel safe playing games online. Therefore, my approach is to instead strengthen game communities by developing tools that promote empathy, diversity, belonging and cooperation among players.

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.