Math, data, and computing in my design practice function as tools to structure complexity and support informed decision-making. While Calculus and Physics in my Bachelor's provided the foundation for cross-disciplinary communication with engineers, my master's development lies in applying data analysis and computing in a designerly way, to reveal patterns, communicate insights, and personalise user experiences.

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My experience spans both quantitative and qualitative approaches. Early work with Processing and Python introduced me to collecting, cleaning, and visualising data, turning raw information into clear insights for stakeholder communication. During the Master’s, however, projects often involved small participant samples or exploratory research questions, shifting my focus toward deepening qualitative methods such as thematic analysis and affinity diagramming. As a result, I have now developed a high sensitivity to scaling my analytical methods based on the project's intent, aligning with mixed-methods approaches in design research, where qualitative and quantitative methods are combined depending on research intent.

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More recently, through projects such as M2.1, the Final Master Project, and Designing Conversational Experiences, I have explored data-flow modelling and adaptive systems. Using technologies such as Arduino and NFC, I investigate how data can flow between physical and digital touchpoints, enabling personalised and context-sensitive interactions. By leveraging algorithms and, where relevant, AI, I explore how technology can support adaptive user experiences and facilitate sense-making. As a designer focused on sense-making, I use these methods not only to inform my own design decisions, but also to help users understand and reflect on their own data through interactive and playful experiences.