Majid Hosseini, PhD
Artificial Intelligence Research Scientist
Expertise
Multimodal learning, human-centric intelligent systems (multimodal learning, real-world implementation science, federated learning, telehealth.
Biography
Dr. Majid Hosseini is an Artificial Intelligence Research Scientist at the Accessible Healthcare through AI-Augmented Decisions (AHeAD) Center at the University of Louisiana at Lafayette.
His research focuses on human-centric intelligent systems, multimodal learning, real-world implementation science, federated learning, and telehealth. His work centers on developing practical AI frameworks for complex real-world applications—ranging from computer vision-based clinical decision support tools for endoscopic sinusitis diagnosis to multimodal physiological sensing systems that monitor stress and driver drowsiness using biometrics, infrared footage, and facial expression analysis.
Publications
Machine Learning-Enhanced Clinical Decision Support for Sinusitis Assessment
Focus: Developing multi-class computer vision and rule-based diagnostic frameworks using nasal endoscopy images to standardise sinusitis detection and lower inter-operator variability.
Citation: Gyawali, D., Hosseini, M., et al. (2025). Machine learning-enhanced clinical decision support for diagnosing sinusitis with nasal endoscopy. PMC / Journal Source.
Multimodal Driver Drowsiness & Stress Detection Datasets
Focus: Collecting and processing multimodal data—combining 3D depth facial video, infrared footage, and physiological biometric signals (heart rate, electrodermal activity, blood oxygen saturation)—to monitor physiological states such as driver fatigue and nurse stress in continuous real-world environments.
Citation: Hosseini, M., Sohrab, F., Gottumukkala, R., Bhupatiraju, R. T., Katragadda, S., Raitoharju, J., Iosifidis, A., & Gabbouj, M. (2025). A multimodal stress detection dataset with facial expressions and physiological signals. arXiv. https://doi.org/10.48550/arxiv.2209.13542
Inception-Based Convolutional Recurrent Neural Networks for Video Frame Prediction
Focus: Proposing deep learning architectures (such as Inception-LSTM) that combine variable kernel sizes in gated recurrent units for unsupervised video frame prediction in computer vision systems.
Citation: Hosseini, M., Maida, A. S., Hosseini, M., & Gottumukkala, R. (2020). Inception LSTM for next-frame video prediction (student abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13809–13810. https://doi.org/10.1609/aaai.v34i10.7176
Entropic Complexity Features for Situational Awareness
Focus: Utilizing entropic complexity metrics as features to evaluate non-linear, interconnected behavioral dynamics and improve situational awareness in dynamical systems.
Citation: Ferdowsi, F., Hosseini, M., & Hosseini, M. (2020). Classification of emergent dynamics in microgrids utilizing an entropic complexity-based feature extractor. Data-Enabled Analytics Journal / Conference Proceedings.