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About

Haofei Wang is a researcher at the intersection of human-computer interaction, brain-computer interfaces (BCIs), and human-robot collaboration, with a particular focus on leveraging gaze and neural signals to enhance how humans interact with intelligent systems. His most celebrated work, a 2015 study on hybrid gaze/EEG brain-computer interfaces (53 citations), demonstrated how combining eye-tracking data with electroencephalography-derived motor imagery signals could enable intuitive robotic arm control for pick-and-place tasks — a meaningful step forward for assistive robotics and rehabilitation technologies. Building on this multimodal sensing expertise, Wang developed a SLAM-based system for estimating three-dimensional gaze targets in mobile environments (2018, 43 citations), moving the field beyond the limitations of screen-based 2D eye tracking. His collaborative work on gaze awareness in assembly tasks (2019, 15 citations) illuminated how shared gaze information between humans can improve teamwork efficiency, with direct implications for human-robot teaming systems. More recently, Wang has explored user engagement measurement in virtual reality rehabilitation (2024), investigating whether behavioral metrics outperform physiological indicators for guiding adaptive robotic therapy. Collectively, his research charts a coherent vision: making human-machine collaboration more natural, responsive, and effective through rich perceptual sensing and intelligent system design.