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About

Farshad Safavi is a rising researcher at the intersection of artificial intelligence, affective computing, and human–robot interaction (HRI). His work centers on enabling robots to perceive, interpret, and respond to human emotions, with a particular focus on facial expression recognition (FER) and EEG-based emotion decoding. Safavi’s most cited paper, “Emerging Frontiers in Human–Robot Interaction” (2024, 26 citations), establishes a forward-looking framework for multimodal communication between humans and robots. He has made notable technical contributions by developing efficient deep learning models—such as the Mix Transformer for FER—that balance high accuracy with computational efficiency, making them suitable for real-time, resource-constrained robotic platforms. His 2023 work on efficient transformers for FER (7 citations) and his 2025 extension to affective HRI (6 citations) demonstrate a sustained commitment to practical, deployable emotion recognition. Safavi has also explored cross-modal emotion recognition, using transformer-based architectures to decode valence and arousal from EEG signals (2024, 5 citations). His research agenda, captured in “New Horizons in Human–Robot Interaction” (2024, 4 citations), emphasizes synergy, cognition, and emotion as pillars of next-generation collaborative robots. With a growing citation footprint and a clear trajectory toward emotionally intelligent autonomous systems, Safavi is shaping the future of socially aware robotics.