Dr. Ebadi’s research concerns applied data science, with a special focus on medical informatics, and covers: 1) development of scalable and intelligent decision support systems, 2) hybrid recommender systems, and 3) hyper graphs analytics and evolution. He has successfully applied such techniques in large-scale real world health applications based on machine learning and deep learning methods. He has also a well-established track of collaboration with other researchers at both national and international levels, and produced several peer-reviewed publications from these collaborative projects.

Research Projects

1. AI-powered tuberculosis screening

2. Construction site progress monitoring

3. Intelligent decision support systems for research evaluation

4. Emerging technologies detection

5. Gender disparity in science

6. AI breast cancer detetion and diagnosis