Majid Hosseini, Ph.D., is an AI research scientist specializing in multimodal learning, healthcare AI, computer vision, edge AI, privacy-aware sensing, cyber-physical systems, and the Internet of Things. He earned his Ph.D. in Computer Science from the University of Louisiana at Lafayette in 2025, where his dissertation focused on robust stress detection using real-world data and multimodal learning.

At the University’s Center for Applied AI, Dr. Hosseini develops intelligent systems for patient monitoring, clinical decision support, stress detection, indoor localization, and medical-image analysis. His work has produced real-time clinical vision systems, multimodal health datasets, and privacy-conscious AI solutions developed in collaboration with healthcare and research partners. He also supervises graduate researchers, co-authors peer-reviewed publications, teaches machine learning and data analytics, and contributes to research proposals.

His research has appeared in venues including Scientific Data, International Forum of Allergy & Rhinology, SN Computer Science, and IEEE and AAAI proceedings. Drawing on experience spanning academia, healthcare, energy, and engineering, he is committed to developing trustworthy AI systems that address consequential real-world problems.