Sahil Anand

Papers

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About

Sahil Anand is a researcher at the intersection of human-robot interaction and advanced communication systems, with a focus on making technology more intuitive and responsive to human behavior. His work explores how robots can autonomously adapt their teaching strategies, particularly in language learning contexts, by dynamically adjusting online lessons based on a learner’s performance and engagement. This contribution, detailed in his most-cited paper “Automatic Adaptation of Online Language Lessons for Robot Tutoring” (2016, 5 citations), lays groundwork for personalized educational robotics. Earlier, Anand investigated next-generation telepresence systems, emphasizing the critical role of non-verbal cues—such as gestures and facial expressions—in remote communication. His 2011 paper on this topic (3 citations) highlights the limitations of traditional audio-video transmission and proposes more immersive, cue-aware platforms. While his citation counts reflect an emerging career, Anand’s work is notable for bridging robotics, education, and human-computer interaction, offering practical pathways toward more natural, adaptive, and socially aware machines.