معرفی
Saeed Amal is an Assistant Research Professor at Northeastern University's College of Engineering and Department of Bioengineering. He leads the Amal Lab for Precision Medicine, focusing on AI-driven healthcare solutions. His primary affiliation is with Northeastern's Roux Institute and he holds academic leadership roles in cardiovascular and cancer research. Previously, he was a Postdoctoral Fellow at Stanford University School of Medicine and held industry roles as VP of R&D at Cardiacx Medical (cardiology startup) and Data Scientist positions at companies like General Electric and Dynamic Yield.
Education:
- B.Sc. in Computer Science, Technion - Israel Institute of Technology (2005)
- M.Sc. in Computer Science (Information Retrieval), University of Haifa (2009)
- Ph.D. in Computer Science (Machine Learning), University of Haifa (2019)
- Postdoctoral Research, Stanford University (2021–Present)
Research Interests: AI applications in healthcare, including deep learning for medical imaging, NLP for clinical text analysis, and multimodal data fusion for cardiovascular and cancer care. His work emphasizes precision medicine, automated disease diagnosis (e.g., prostate and breast cancer), and improving patient outcomes through AI-driven tools.
Key Contributions: Developed AI tools for cardiovascular disease prediction using multimodal data fusion, led initiatives in digital pathology for cancer grading, and pioneered the use of generative AI in Gleason pattern segmentation. His lab has published extensively on machine learning models for adverse event prediction in surgery and healthcare decision-making visualization.
Awards:
- Emerging Visionary Award (Northeastern University NAI, 2025)
Grants and Collaborations: Partnered with Santovia Path AI and Prima CARE on AI tools for cancer diagnosis. His work is supported by Northeastern's research initiatives and collaborations with institutions like Stanford University School of Medicine.
Labs/Teams: Director of The Amal Lab for Precision Medicine, focusing on AI and multimodal data integration for disease care.
