Kulin Patel

University of Maryland, Baltimore County, University of Maryland, Baltimore

Papers

Total Citations

H-Index

About

Kulin Patel is a rising researcher at the intersection of affective computing and efficient deep learning, with a focus on enabling machines to understand human emotions. His work centers on two key modalities: facial expression recognition (FER) and electroencephalography (EEG)-based emotion recognition. Patel’s major contributions include pioneering the use of efficient Transformer architectures for FER, specifically targeting real-time applications in healthcare—such as pain assessment and mental disorder diagnosis—and in affective human-robot interaction. His 2023 paper, “Towards Efficient Deep Learning Models for Facial Expression Recognition using Transformers,” has garnered 7 citations, while his 2025 work on an efficient Mix Transformer for affective human-robot interaction has already earned 6 citations, underscoring the timeliness of his research. In 2024, Patel extended Transformer-based methods to EEG signals, predicting valence and arousal levels with a tailored model, which has received 5 citations. His notable achievement lies in balancing model accuracy with computational efficiency, a critical step for deploying emotion recognition in resource-constrained, interactive systems. Patel’s work is paving the way for more responsive and socially aware AI.