Mononito Goswami
Papers
1
Total Citations
10
H-Index
1
About
Mononito Goswami is a researcher at the intersection of machine learning, healthcare, and human-robot interaction, with a focus on building socially aware AI systems. His work explores how intelligent agents can better understand and respond to human behavior, particularly in sensitive contexts like child development and clinical decision-making. In his highly cited 2020 paper, “Towards Social & Engaging Peer Learning,” Goswami tackled a critical challenge in educational robotics: enabling robots to detect when children are disengaged or backchanneling during peer learning interactions. By developing predictive models for these subtle social cues, his research helps robots behave more naturally and earn children’s trust—a key requirement for effective language development tools. This work, which has garnered 10 citations, exemplifies his broader mission to make AI not just intelligent, but socially perceptive. Goswami’s contributions sit at the crossroads of affective computing, interactive AI, and developmental psychology, offering practical pathways for robots to become more empathetic learning companions. His research continues to push the boundaries of how machines perceive and respond to human social signals, with implications for education, healthcare, and beyond.
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Top Papers
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