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Shikha Tripathi is a prominent researcher specializing in affective computing, human-computer interaction, and facial expression analysis. Her work has significantly advanced the field of automated emotion recognition, addressing key real-world challenges such as variations in pose, illumination, age, and occlusion that complicate accurate facial analysis. Tripathi's most influential contribution, "Real-time emotion recognition from facial images using Raspberry Pi II" (2016), has garnered 115 citations, demonstrating its widespread impact on embedded and resource-constrained emotion recognition systems. Her research consistently pushes boundaries by developing robust, intelligent systems capable of functioning under challenging conditions — from masked faces to non-frontal 3D imagery — making her work highly applicable to real-world deployments. Her exploration of multimodal emotion recognition, integrating facial, audio, and gestural cues, reflects a holistic understanding of human communication, particularly in the context of human-robot interaction. More recently, her work on emotion intensity classification signals a meaningful step beyond binary emotion detection toward nuanced affective understanding. With over 230 cumulative citations across her portfolio, Tripathi's research has meaningfully shaped how machines perceive and respond to human emotions, contributing foundational methods that continue to inform robotics, healthcare, and intelligent interface design.