Artin Majdi, PhD

Artin is a researcher specializing in computer vision and machine learning, with a focus on medical image analysis. He holds a Ph.D. in Electrical and Computer Engineering and has extensive expertise in designing and deploying end-to-end deep learning systems using advanced architectures like Convolutional Neural Networks (CNNs) and Vision Transformers. His background includes a strong track record in medical image processing, computer vision and large language models, as well as experience leading research projects, supervising students, and securing grant funding.

At George Mason University, he joins the interdisciplinary EAS-ID project. This initiative is developing an AI-powered mobile platform for the equitable detection of bruises across all skin tones, a critical tool for documenting intimate partner violence. Working at the intersection of nursing science, computer vision, and healthcare equity, the project uses multi-spectral imaging and deep learning to detect, age, and document bruises. This technology addresses a significant disparity where traditional methods often fail individuals with darker skin tones, aiming to improve both healthcare outcomes and legal documentation for survivors of violence.

Research

Research Interests

• Advanced Computer Vision: Novel deep learning architectures for image segmentation, object detection, and classification in complex scenes.
• Medical Image Computing: AI-driven diagnostic and prognostic tools for radiology, pathology, and ophthalmology; quantitative imaging biomarkers.
• Image-based Predictive Modeling: Leveraging computer vision for predicting disease progression, treatment response, and patient outcomes.
• Multimodal Data Fusion for Imaging: Combining image data with other modalities (e.g., clinical records, genomics) for enhanced analysis.
• Explainable AI (XAI) in Computer Vision: Developing interpretable models for clinical decision support in image-based applications.
• Uncertainty Quantification in Vision Systems: Assessing and communicating model confidence in image analysis tasks.
• Generative Models for Image Synthesis & Augmentation: Creating realistic medical images for training and validation.

Select Publications

Journal Articles

• Majdi, M.S., Keerthivasan, M.B., Rutt, B.K., Zahr, N.M., odriguez, J.J., Saranathan, M., 2020. Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural Networks. Magnetic Resonance Imaging 73, 45–54. https://doi.org/10.1016/j.mri.2020.08.005 
• Dadashazar, H., Crosbie, E., Majdi, M.S., Panahi, M., Moghaddam, M.A., Behrangi, A., Brunke, M., Zeng, X., Jonsson, H.H., Sorooshian, A., 2020. Stratocumulus Cloud Clearings: Statistics From Satellites, Reanalysis Models, and Airborne Measurements. Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-20-4637-2020 
• Das, D., Iyengar, M.S., Majdi, M.S., Rodriguez, J.J., Alsayed, M., 2024. Deep Learning for Thyroid Nodule Examination: A Technical Review. Artif Intell Rev 57, 47. https://doi.org/10.1007/s10462-023-10635-9 

Patents

• M. S. Majdi, J. N. Hyde, J. J. Rodriguez, E. M. Sternberg, and, I. J. R. Runyon, “Monitoring of Autonomic Nervous System Activity Through Sweat Pore Activation,” US20230346296A1, Nov. 02, 2023 Accessed: Mar. 20, 2024. [Online]. Available: https://patents.google.com/patent/US20230346296A1/en 
• M. S. Majdi, E. M. Sternberg, J. N. Hyde, J. J. Rodriguez, Y.-J. Son, and Y. Chen, “Imaging Human Respiratory Gas Patterns to Determine Volume, Rate and Carbon Dioxide Concentration,” US20230301546A1, Sep. 28, 2023 Accessed: Aug. 08, 2024. [Online]. Available: https://patents.google.com/patent/US20230301546A1/en 

Conference/Scholarly Presentations

• Majdi MS, Salman KN, Morris MF, et al (2020) Deep Learning Classification of Chest X-Ray Images. In: Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, Albuquerque, NM, USA, pp 116–119
• Majdi MS, Ram S, Gill JT, Rodriguez JJ (2018) Drive-Net: Convolutional Network for Driver Distraction Detection. In: 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, pp 1–4
• Ram S, Majdi MS, Rodriguez JJ, et al (2018) Classification of Primary Cilia in Microscopy Images Using Convolutional Neural Random Forests. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation. Institute of Electrical and Electronics Engineers Inc., pp 89–92
• Majdi MS, Fatemizadeh E (2015) Laplacian Mixture Model Point Based Registration. In: 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE, Tehran, Iran, pp 57–60

Honors and Awards

• 2022, Roots for Resilience Scholarship (co-led by AIR, CyVerse, & DS)
• 2022, Tech Launch Arizona Impact Software/App Award 
• 2022, Software Carpentry Instructor 
• 2019, Microsoft Social Innovation Award 
• 2018, Thomas R Brown Distinguished Scholarship 
• 2013, Ranked in the top 1% in nationwide Graduate School Entrance Exam (Iran) 
• 2008, Ranked in the top 1% in nationwide University Entrance Exam (Iran)

Degree

• PhD, Electrical & Computer Engineering, Minor: Entrepreneurship University of Arizona