To deeply understand the user experience around machine learning in microscopy, I conducted interviews with six participants across different levels of technical expertise. After each session, I mapped out the insights on a shared board using post-its, organizing observations around key moments in the workflow. This visual mapping allowed patterns and recurring pain points to emerge quickly.

Through this process, I identified a clear sequence of core steps users needed to go through: annotating datasets, training the model, receiving augmented data, processing 2D segmentations, and finally extracting insights. Each of these steps came with its own set of mental models, expectations, and areas of uncertainty.

To make sense of the complexity, I created a synthesis of the interviews that distilled user goals, frustrations, and informational gaps. This synthesis helped me uncover what users needed to know at each step and where they struggled most—especially with confidence, understanding outcomes, and knowing what would happen next.

I then built a detailed task analysis of the entire machine learning process, breaking it down step by step from the user's point of view. This task analysis became a foundational reference that guided design decisions throughout the project. It helped me align the interface with users’ mental models and ensure that each part of the experience felt purposeful, clear, and supportive—even within the tight constraints of our MVP.