Image Processing Specialist

Dr. Fahimeh Mohagheghian is an Image Processing Specialist in the Data Analytics Center (DAC) at UVA Research Computing. She holds a PhD and MSc in Biomedical Engineering and a BSc in Electrical Engineering. She brings over a decade of experience developing AI/ML, signal processing, and medical imaging methodologies for healthcare research.

Her expertise includes physiological signal analysis (EEG, iEEG, ICP, PPG, ECG), deep learning, generative models, and the development of scalable data analytics pipelines for large biomedical datasets. She collaborates closely with clinical and research teams to design robust, clinically deployable computational solutions for neurocritical care, neuroscience, and wearable health technologies. Her work bridges advanced artificial intelligence methodologies with translational clinical research to enable reproducible, data-driven decision support systems.

Google Scholar

Selected Publications

• Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates, F. Mohagheghian, S. Jiang, M. Connolly, E. Sproule, R. Gross, X. Hu, A. Devergnas, Physiological Measurement, 2025.
• Atrial fibrillation detection on reconstructed photoplethysmography signals collected from a smartwatch using a denoising autoencoder, Mohagheghian, F., Han, D., McManus, D., Chon, K., Expert Syst. Appl., 2024.
• Noise Reduction in Photoplethysmography Signals using a Convolutional Denoising Autoencoder with Unconventional Training Scheme, Mohagheghian, F., McManus, D., Chon, K., IEEE Transactions on Biomedical Engineering, 2023.
• Optimized signal quality assessment for photoplethysmogram signals using feature selection, Mohagheghian, F., Han, D., McManus, D., Chon, K., IEEE Transactions on Biomedical