Saeed Amal
Assistant Research Professor, Bioengineering
Research Focus
Artificial intelligence including deep learning and machine learning for healthcare, natural language processing (NLP) and image processing for healthcare, recommender systems for healthcare
About
Saeed Amal is an Assistant Research Professor at Northeastern University. Amal was a Postdoctoral Fellow at the Stanford University School of Medicine where he focused on the applications of artificial intelligence to improve healthcare, specifically care for cardiovascular disease. He received his PhD and MSc degree in Computer Science from the University of Haifa and a BSc degree in Computer Science from Technion.
Amal has vast leadership experience in applied research from the tech industry and is a former VP of an R&D data medical startup in the field of cardiology. His research interests are the healthcare applications for artificial intelligence, including deep learning and machine learning, natural language processing (NLP), image processing, and recommender systems.
Research Overview
Artificial intelligence including deep learning and machine learning for healthcare, natural language processing (NLP) and image processing for healthcare, recommender systems for healthcare
Department Research Areas
Selected Publications
- Balasubramanian AA, Al-Heejawi SMA, Singh A, Breggia A, Ahmad B, Christman R, Ryan ST, Amal S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers. 2024; 16(12):2222. https://doi.org/10.3390/cancers16122222
- Co-first author on – Ghanzouri, I., Amal, S., Ho, V., Safarnejad, L., Cabot, J., Brown-Johnson, C. G., … & Ross, E. G. (2022). Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. Scientific reports, 12(1), 1-11.
- Amal, S., Safarnejad, L., Omiye, J. A., Ghanzouri, I., Cabot, J. H., & Ross, E. G. (2022). Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care. Frontiers in Cardiovascular Medicine, 9.
- Amal, S., Minkov, E., & Kuflik, T. (2021). Person Entity Profiling Framework: Identifying, Integrating and Visualizing Online Freely Available Entity-Related Information. arXiv e-prints, arXiv-2110.
- Amal, S., Adam, M., Brusilovsky, P., Minkov, E., & Kuflik, T. (2020, September). Personalized Multifaceted Visualization of Scholars Profiles. In Proceedings of the International Conference on Advanced Visual Interfaces (pp. 1-3).
- Amal, S., Adam, M., Brusilovsky, P., Minkov, E., Segal, Z., & Kuflik, T. (2020, July). Visualizing Personalized Multifaceted ad-hoc Social Network. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 101-102).
- Amal, S., Adam, M., Brusilovsky, P., Minkov, E., Segal, Z., & Kuflik, T. V. (2020, March). Demonstrating personalized multifaceted visualization of people recommendation to conference participants. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion (pp. 49-50).
- Amal, S., Tsai, C. H., Brusilovsky, P., Kuflik, T., & Minkov, E. (2019). Relational social recommendation: Application to the academic domain. Expert Systems with Applications, 124, 182-195.
- Amal, S., Adam, M., Brusilovsky, P., Minkov, E., & Kuflik, T. (2019, March). Enhancing explainability of social recommendation using 2D graphs and word cloud visualizations. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 21-22).
- Amal, S., KuFlik, T., & Minkov, E. (2017, July). Harvesting entity-relation social networks from the web: Potential and challenges. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 351-352).
Jul 31, 2024
New Research Shows AI Can Revolutionize Breast Cancer Diagnosis
BioE Assistant Research Professor Saeed Amal was featured in the Lagatar News article “New Research Shows AI Can Revolutionize Breast Cancer Diagnosis.”
Jun 26, 2024
New AI Architecture Can Detect Breast Cancer With a Near-Perfect Accuracy Rate
BioE Assistant Research Professor Saeed Amal and his research team have developed a new AI architecture that has detected breast cancer with a 99.7% accuracy rate. His research was published in the journal Cancers. He has submitted an invention disclosure with the Center for Research Innovation on the idea.
Jan 11, 2024
Detecting Prostate Cancer Faster With AI
BioE Assistant Research Professor Saeed Amal has developed a new AI-powered web-based tool that will be able to detect prostate cancer faster.