Dr Raja Ebsim

Position:	Research Associate
ORCID::	0000-0002-1875-5503
	Google Scholar Linkedin
Division:	Informatics, Imaging and Data Sciences, the School of Health Sciences.
Faculty:	Biology, Medicine, and Health, The University of Manchester
Email:	`Raja.Ebsim at manchester.ac.uk`
Phone:	`(+44) 161 275 1245`
Location:	Room G605, Stopford Building, (Building 79 on this Campus Map)
	Oxford Road,
	Manchester, UK M13 9PT

Research Interests

Medical image computing.
Statistical models of shape and appearance.
Machine learning and pattern recognition.
Deep learning and computer vision.
Data science.

My current post @ UoM

I am a member of a cross-disciplinary and cross-institution research team of Project AUGMENT, under the supervision of Prof Tim Cootes and Dr Claudia Lindner.
My work involves developing models for specific structures to support medical research, aimed at understanding the variation in shape and appearance of bones across different populations, and the changes caused by disease.

Education

PhD in Computer Science 2018, University Of Manchester/UK. PDF , Webpage
MSc in Informatics with distinction, University Of Edinburgh/UK.
BSc in Computer Engineering (top of class), University Of Tripoli/ Libya.

Past Teaching Experience

Courses (as a graduate teaching assistant @ University of Manchester 2015-2017 )

Undergrad courses:
- Fundamentals of Artificial Intelligence.
- Machine Learning and Optimisation.
- Fundamentals of Computation.
- Fundamentals of Databases.
Postgrad courses:
- Foundations of Machine Learning.
- Data Engineering.

Some Books I enjoyed and would recommend

Thinking, Fast and Slow by Daniel Kahneman

Psychological aspects of the way we deal with stats.

The Master Algorithm by Pedro Domingos

Non-technical detailed overview of the field of Machine learning (ML)

Emotional Agility by Susan David

very good personal self-development book

Pattern recognition and machine learning by Christopher Bishop

Essential reading on ML theory.

Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurelien Geron

Exactly as its title says.