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About

Pratiksha Thaker’s research lies at the intersection of natural language processing and human-robot interaction, with a focus on enabling robots to understand and communicate with humans more effectively. Her major contributions center on developing information-theoretic approaches to human-robot dialog, where she pioneered methods for robots to actively clarify ambiguous commands by seeking the most informative feedback from human partners. This work, detailed in her highly cited papers from 2012 and 2013, addresses the fundamental challenge of bridging the gap between human language’s inherent ambiguity and robots’ limited perceptual capabilities. Thaker also made significant strides in grounded language learning, creating techniques that allow robots to learn word meanings from unaligned parallel data—a breakthrough that reduces the need for detailed, pre-annotated training examples. Her 2013 paper on this topic has garnered 59 citations, reflecting its impact on the field. Collectively, her publications have accumulated over 190 citations, establishing her as a key voice in making human-robot communication more robust and natural. Thaker’s work continues to influence researchers developing interactive robots that can learn and adapt through conversation.