Personal profile


Ossama Alshabrawy is an assistant professor in the Department of Computer and Information Sciences. Ossama is interested in AI, Machine Learning/Deep Learning and Data Science, in particular around the domains of healthcare applications.

Ossama's recent work focuses on analysing the social interactions of people with Depression in terms of analysing the speech and physical activities of the patients and extract digital markers from the data. He is also interested in analysing the acceleromter data of people with metabolic diseases especially Type-2 Diabetes and Cardiovascular disease. More recently, he is focusing on the assessment of digital markers and treatment response evaluation from MRI images for Bipolar disorder.

Previously, Ossama was a research associate in the School of Computing, Newcastle University working on designing and assessment of deep learning networks and other machine learning models for predicting disease state from a combination of free-living movement data(accelerometry), trained over “Big Data” cohorts of individuals. Prior to that Ossama obtained his PhD degree in data science for healthcare from the School of Computing, Newcastle University. He obtained MSc in Computer Science from Port-Said University, Egypt and BSc in Maths and Computer Science from Damietta University, Egypt.

Research interests

AI, Machine Learning/Deep Learning, Data Science, Wearable Computing, Digital Health

Further Information

Campus Address

B114, Ellison Building
Northumbria University
Newcastle upon Tyne

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Education/Academic qualification

Computing, PhD, Deep Learning-Based Automatic Analysis of Social Interactions from Wearable Data for Healthcare Applications, Newcastle University

21 Sept 201530 Sept 2019

Award Date: 2 Dec 2019


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Collaborations and top research areas from the last five years

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