Dr. Elhabian, the valedictorian of her class, received her BSc. and MSc. in Computer Science (with an emphasis on mathematics, signal and image processing, pattern recognition, and computer graphics) from the Faculty of Computers and Artificial Intelligence, Cairo University (FCI-CU), Egypt, in 2002 and 2005, respectively. From Fall 2002 to Summer 2007, she had the privilege of serving as a computer science assistant lecturer at FCI-CU where she received the best teaching assistant award from the Cairo University in 2005. She has also conducted teaching and mentoring undergraduate as well as graduate students in topics related to computer vision, image processing, signal processing and pattern recognition in FCI-CU and the Computer Vision and Image Processing (CVIP) Lab at the University of Louisville.
Dr. Elhabian received her PhD in Electrical and Computer Engineering (ECE) (with an emphasis on statistical and mathematical modeling, computer vision, and machine learning) from the University of Louisville (UofL), USA, in Fall 2012. She was a postdoctoral associate from 2013 to 2015, and a research computer scientist from 2016 to early 2017, both at the Scientific Computing and Imaging (SCI) Institute, University of Utah. Her postdoctoral training focused on medical imaging, computer vision, and machine learning. Dr. Elhabian is currently a Research Assistant Professor in the School of Computing and a faculty member in the SCI Institute.
Dr Elhabian's research expertise lies in developing theoretical foundations and computational methods for inferring problem-driven semantic information from imaging data. Dr. Elhabian's research is an interdisciplinary endeavor that spans medical image analysis, machine learning, and optimization. She is most interested in research that integrates multiple scientific disciplines and views of image understanding and machine learning problems. Much of her current focus is on probabilistic modeling and deep learning, and how the computational solutions that emerge in this space can enrich statistical shape analysis, subspace learning, generative image and shape modeling, and anatomy reconstruction from sparse imaging. With her interest in the implications of advances in these fields for society and industry, Dr. Elhabian establishes her research around real-world problems that entail collaborating with scientists and domain experts of different disciplines and backgrounds to conduct interdisciplinary research projects.
- Medical image analysis
- Machine learning (in particular deep learning)
- Image processing
- Shape analysis
- Computer vision