Interactive Machine Learning
Bridging the gap between design and engineering practice by scrutinizing the technical functionality of a software paradigm
Research & Design
A great source of tension between the designers who conceive of an idea and the engineers who implement it is that so often, designers lack sufficient technical knowledge of user interface paradigms to understand what is possible and cost-effective to implement. In an independent study under the supervision of Andy Ko, I sought to explore the field of human-centered machine learning from a technically-focused perspective in order to better understand the viability of the ideas inspired by my research and to practice communicating ideas to an engineering audience (a skill that would prove crucial for my capstone with JPL). The final product was a technical specification for a neuroimaging analysis tool that uses interactive machine learning to help neuroscientists detect and classify stroke in MRI images.