Soheyla Amirian

Personal Quote

Soheyla Amirian — Applied AI leader who combines technical AI research, real-world deployment, research leadership, developer education, and community engagement.

Faculty Bio

Dr. Soheyla Amirian is an Assistant Professor at the Seidenberg School of Computer Science and Information Systems at Pace University and Director of the Applied Machine Intelligence Initiatives & Education (AMIIE) Lab. She leads multidisciplinary teams developing and evaluating AI and machine learning solutions across computer vision, multimodal AI, responsible and explainable AI, AI for healthcare, and AI-powered education.
Dr. Amirian earned her Ph.D. in Computer Science from the University of Georgia, where her research focused on deep learning, computer vision, and image and video understanding. Her current work bridges AI research and real-world applications, with an emphasis on developing reliable, interpretable, and responsible AI systems.
Her research and professional contributions have been recognized through NIH/NIA-funded research, the Wilson Center Faculty Fellowship at Pace University, the IEEE Atlanta Section Outstanding Educator Award, and an NVIDIA GPU Grant. She has authored 25+ peer-reviewed publications and regularly contributes to the AI and computing community through invited talks, conference leadership, technical programs, and mentorship.
Dr. Amirian has served in leadership and program roles for conferences including IEEE EMBC, CSCI, and CSCE, and previously served as a Faculty Lecturer and AI Research Fellow at the University of Georgia. Her work combines AI research, education, mentorship, and community engagement to advance the responsible development and practical adoption of artificial intelligence.

Awards and Honors

• Pace University, 2026, Kenan Fund for Travel
• PennAITech, 2024, NIH/ National Institute on Aging (NIA)
• IEEE, 2023, IEEE Atlanta Section Outstanding Educator Award

PhD, University of Georgia, Georgia, USA, 2021
Computer Science

Grants, Sponsored Research and Contracts

Fair and Explainable AI for Autonomous Diagnosis of Joint Osteoarthritis in Aging Population
Amirian, S. 2024 - 2025. The Helene T. and Grant M. Wilson Center for Social Entrepreneurship, Pace University. Funded.

Past Courses

CS 241: Data Structures/Algorithms
CS 627: Artificial Intelligence
CS 802: Research Seminar

Publications

State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis
Amirian, S., Gao, F., Littlefield, N., Hill, J. H., Yates, A. J., Plate, J. F., Pantanowitz, L., Rashidi, H. H. & Tafti, A. P. (2025). IEEE Access. Vol 13 , pages 58229-58263. https://doi.org/10.1109/access.2025.3555543

Predicting cancer survival at different stages: Insights from fair and explainable machine learning approaches
Kamble, T. S., Wang, H., Myers, N., Littlefield, N., Reid, L., McCarthy, C. S., Lee, Y. J., Liu, H., Pantanowitz, L., Amirian, S., Rashidi, H. H. & Tafti, A. P. (2025). International Journal of Medical Informatics. Vol 197 , pages 105822. https://doi.org/10.1016/j.ijmedinf.2025.105822

Development and Validation of a Mobile Phone Application for Measuring Knee Range of Motion
Gong, M. F., Finger, L. E., Letter, C., Amirian, S., Parmanto, B., O'Malley, M., Klatt, B. A., Tafti, A. P. & Plate, J. F. (2025). The Journal of Knee Surgery. Vol 38 (Issue 01) , pages 022-027. https://doi.org/10.1055/a-2388-0812

Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading
Littlefield, N., Amirian, S., Biehl, J., Andrews, E. G., Kann, M., Myers, N., Reid, L., Yates, A. J., McGrory, B. J., Parmanto, B., Seyler, T. M., Plate, J. F., Rashidi, H. H. & Tafti, A. P. (2024). Journal of the American Medical Informatics Association. Vol 31 (Issue 11) , pages 2668-2678. https://doi.org/10.1093/jamia/ocae246

Author comment: Advancing psychosocial disability and psychosocial rehabilitation research through large language models and computational text mining — R2/PR7
Amirian, S. (2024). https://doi.org/10.1017/gmh.2024.114.pr7

Evolutionary CNN-based architectures with attention mechanisms for enhanced image classification
Shams, A., Becker, K., Becker, D., Amirian, S. & Rasheed, K. (2024). Athens , United States:De Gruyter. , pages 107-132. https://doi.org/10.1515/9783111344126-006

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare
Siddiqui, I. A., Littlefield, N., Carlson, L. A., Gong, M., Chhabra, A., Menezes, Z., Mastorakos, G. M., Thakar, S. M., Abedian, M., Lohse, I., Weiss, K. R., Plate, J. F., Moradi, H., Amirian, S. & Tafti, A. P. (2024). Scientific Reports. Vol 14 (Issue 1) https://doi.org/10.1038/s41598-024-66873-6

Learning Unbiased Image Segmentation: A Case Study with Plain Knee Radiographs
Littlefield, N., Plate, J. F., Weiss, K. R., Lohse, I., Chhabra, A., Siddiqui, I. A., Menezes, Z., Mastorakos, G., Mehul Thakar, S., Abedian, M., Gong, M. F., Carlson, L. A., Moradi, H., Amirian, S. & Tafti, A. P. (2023). , pages 1-5. https://doi.org/10.1109/bhi58575.2023.10313433

Evolving Efficient CNN Based Model for Image Classification
Shams, A., Becker, D., Becker, K., Amirian, S. & Rasheed, K. (2023). , pages 228-235. https://doi.org/10.1109/csce60160.2023.00041

Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects
Amirian, S., Carlson, L. A., Gong, M. F., Lohse, I., Weiss, K. R., Plate, J. F. & Tafti, A. P. (2023). , pages 1374-1380. https://doi.org/10.1109/csce60160.2023.00230

AI Fairness in Hip Bony Anatomy Segmentation: Analyzing and Mitigating Gender and Racial Bias in Plain Radiography Analysis
Littlefield, N., Plate, J. F., Weiss, K. R., Lohse, I., Chhabra, A., Siddiqui, I. A., Menezes, Z., Mastorakos, G., Amirian, S., Moradi, H. & Tafti, A. P. (2023). , pages 714-716. https://doi.org/10.1109/ichi57859.2023.00130

Enforcing Explainable Deep Few-Shot Learning to Analyze Plain Knee Radiographs: Data from the Osteoarthritis Initiative
Littlefield, N., Moradi, H., Amirian, S., Kremers, H. M., Plate, J. F. & Tafti, A. P. (2023). , pages 252-260. https://doi.org/10.1109/ichi57859.2023.00042

Word Embedding Neural Networks to Advance Knee Osteoarthritis Research
Amirian, S., Ghazaleh, H., Assefi, M., Kremers, H. M., Arabnia, H. R., Plate, J. F. & Tafti, A. P. (2022). , pages 289-292. https://doi.org/10.1109/csci58124.2022.00055

Generative Adversarial Network Applications in Creating a Meta-Universe
Amirian, S., Taha, T. R., Rasheed, K. & Arabnia, H. R. (2021). , pages 175-179. https://doi.org/10.1109/csci54926.2021.00101

Presentations

AI in Halthcare
Amirian, S. (2025). Icahn School of Medicine at Mount Sinai, Bado Lab. Tisch Cancer Institute,

AI Fairness to Empower Equity in Healthcare: Addressing Bias in AI-Powered Medical Image Segmentation
Amirian, S. (2024). the 2024 International Conference on Computational Science and Computational Intelligence. Las Vegas, NV, USA.

Fair and Responsible AI in Medical Imaging Informatics
Amirian, S. (2024). The Seidenberg Annual Research Day. Pace University,

Generative Artificial Intelligence and Enhanced Analysis of Scientific Literature: Advanced Clinical Text Mining on PubMed
Amirian, S. (2024). The GWU Biomedical Informatics Center, CTSI-CN, and Washington DC VA Joint Informatics Seminar Series. George Washington University,

Intellectual Property

Media Contributions

The tenth newsletter of the Penn Artificial Intelligence and Technology Collaboratory for Healthy Aging (PennAITech) [Other], December2024
The tenth newsletter of the Penn Artificial Intelligence and Technology Collaboratory for Healthy Aging (PennAITech), Dec 2024
https://www.canva.com/design/DAGWS8Q5jhY/xZTrdwmWhBEzJnuEqAaXCw/view?utm_content=DAGWS8Q5jhY&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=hbfd4bcf2f1#7

Pace University [Internet], September42024
Pace's Newest AI Expert, Sep 2024
https://www.pace.edu/news/introducing-paces-newest-ai-expert

Professional Memberships

• IEEE [Member]
• International Conference on the AI Revolution: Research, Ethics, and Society [STEERING COMMITTEE CO-CHAIR]
• Institute of Electrical and Electronics Engineers (IEEE) [Women in Computing of Atlanta Chair, April 2023- present]