̨ÍåSWAG

Personal Quote

"Passionate about Computer Vision, Deep Learning, and AI fairness. Skilled at simplifying complex concepts, building trust, and collaborating to apply technical expertise to real-world AI challenges. Ambitious and detail-oriented."

Faculty Bio

Dr. Soheyla Amirian is currently an Assistant Professor at the Seidenberg School of Computer Science and Information Systems, ̨ÍåSWAGUniversity. She leads the Applied Machine Intelligence Initiatives & Education (AMIIE) Laboratory, collaborating with a multidisciplinary team of faculty, students, and investigators to design, build, validate, and deploy AI algorithms in various real-world applications, including public health, imaging informatics, and AI-powered education. Dr. Amirian earned her BSc, MSc, and PhD in Computer Science, specializing in AI and deep learning computer vision. She has received numerous accolades, including the 2019 International Conference on Computational Science and Computational Intelligence (CSCI) Outstanding Achievement award, the 2021 UGA Outstanding Teaching Assistant award, the NVIDIA GPU award, and the ACM Richard TAPIA Conference Scholarship in 2020 and 2022. Additionally, she was a finalist for the 2020 NCWIT Collegiate Award and has authored over 25 peer-reviewed publications. Dr. Amirian has organized several conferences and tutorials on computational intelligence, such as ISVC and IEEE ICHI, and has served as a Program Committee member at IEEE ICHI and Co-Chair of Research Tracks at the World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE) and the International Conference on Computational Science & Computational Intelligence (CSCI). She was a faculty fellow at the Institute for Artificial Intelligence and a faculty lecturer at the School of Computing, University of Georgia for three years. In 2023, she received the IEEE Atlanta Section Outstanding Educator Award. Most recently, she was awarded a grant from the NIH/National Institute on Aging (NIA).

Awards and Honors

• 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

Research Interest

Technical: Explainable, Interpretable, and Accountable AI and Machine Learning

Application: Health Informatics

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, ̨ÍåSWAGUniversity. Funded.

Past Courses

CS 241: Data Structures/Algorithms
CS 627: Artificial Intelligence

Publications

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.

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)

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.

Evolving Efficient CNN Based Model for Image Classification
Shams, A., Becker, D., Becker, K., Amirian, S. & Rasheed, K. (2023). , pages 228-235.

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.

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.

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.

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.

Generative Adversarial Network Applications in Creating a Meta-Universe
Amirian, S., Taha, T. R., Rasheed, K. & Arabnia, H. R. (2021). , pages 175-179.

The Use of Video Captioning for Fostering Physical Activity
Amirian, S., Farahani, A., Arabnia, H. R., Rasheed, K. & Taha, T. R. (2020). , pages 611-614.

Presentations

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.

Amirian, S. (2024). The Seidenberg Annual Research Day. ̨ÍåSWAGUniversity,

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,

Professional Memberships

• The 19th International Symposium on Visual Computing [International Program Committee member]
• IEEE [member]
• Institute of Electrical and Electronics Engineers (IEEE) [Women in Computing of Atlanta Chair, April 2023- present]