Mike Schaekermann

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About

Mike Schaekermann is a researcher whose work sits at the intersection of human-computer interaction, crowdsourcing, and machine learning, with a particular focus on designing systems that foster curiosity and enable high-quality human-AI collaboration. His most-cited paper, "Designing for Curiosity" (2017, 3 citations), lays foundational groundwork for understanding how curiosity can be systematically integrated into interactive technologies—from health apps to artistic displays—to motivate learning and sustained engagement. Beyond this, Schaekermann is best known for his pioneering contributions to "human-in-the-loop" machine learning, where he has developed innovative methods for leveraging crowd workers to generate reliable training data and evaluate AI systems. His work on "microtasking" and "crowd truth" has been instrumental in advancing the reliability of human judgment in AI pipelines, earning him recognition as a leading voice in the field. With a growing citation impact and a reputation for bridging theoretical insights with practical design frameworks, Schaekermann’s research continues to shape how we build systems that are not only intelligent but also deeply engaging and trustworthy.