Max Robotics — Global Service Robot Directory + US Certification

About

Erin Solovey is a human-computer interaction researcher whose work spans tangible interfaces, brain-computer interaction, and human-robot collaboration. She is perhaps best known for her foundational contributions to adaptive systems driven by neural signals, most notably through the **Brainput** system (2012, 85 citations), which pioneered the use of functional near-infrared spectroscopy (fNIRS) to detect cognitive multitasking in real time and dynamically adjust robot behavior to better support users. This work built on her broader interest in sensing and modeling cognitive states, including her highly cited comparative study of tangible versus graphical programming languages in informal science education (2009, 280 citations), which provided rare empirical evidence on the educational effectiveness of tangible interaction. Solovey's research has increasingly focused on the design and dynamics of human-agent and human-robot teams, examining how team structure, trust, and adaptive interfaces influence performance and workload in complex supervisory control scenarios. Her investigations into inconsistent virtual agents and multimodal cognitive modeling reflect a commitment to understanding how humans and intelligent systems can collaborate more effectively. With work spanning brain metrics, teamwork modeling, and adaptive user interfaces, Solovey has made meaningful contributions to making computational systems more responsive to the full cognitive richness of their human partners.