Math, data, and computing in my design practice function as tools to structure complexity and support informed decision-making. While a background in 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 mainly qualitative approaches. 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.

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More recently, I have explored conceptual data-flow modelling and adaptive systems. Using technologies such as Arduino and NFC, I experienced how data can flow between physical and digital touchpoints, enabling personalised and context-sensitive interactions. By being aware of the possibilities in leveraging algorithms and, where relevant, AI, I can conceptually explore how technology can support adaptive user experiences and facilitate sense-making.