SUNY Upstate Medical University | United States
Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.
Featured Publications
Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.
Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.
Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.
Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.
Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.