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Dr Ossama Alshabrawy

Assistant Professor

Department: Computer and Information Sciences

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.

Ossama Alshabrawy

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

  • Please visit the Pure Research Information Portal for further information
  • Deep learning-based automated speech detection as a marker of social functioning in late-life depression, Little, B., Alshabrawy, O., Stow, D., Ferrier, I., McNaney, R., Jackson, D., Ladha, K., Ladha, C., Ploetz, T., Bacardit, J., Olivier, P., Gallagher, P., O'Brien, J. 1 Jul 2021, In: Psychological Medicine
  • Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort, Lam, B., Catt, M., Cassidy, S., Bacardit, J., Darke, P., Butterfield, S., Alshabrawy, O., Trenell, M., Missier, P. 19 Mar 2021, In: JMIR Diabetes
  • Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM, Parathai, P., Tengtrairat, N., Woo, W., Abdullah, M., Rafiee, G., Alshabrawy, O. 5 Aug 2020, In: Sensors
  • Blind separation of underdetermined mixtures with additive white and pink noises, Alshabrawy, O., Hassanien, A., Awad, W., Salama, A. 10 Oct 2014, 13th International Conference on Hybrid Intelligent Systems, HIS 2013, Piscataway, Institute of Electrical and Electronics Engineers Inc.
  • Underdetermined Blind Separation of Mixtures of an Unknown Number of Sources with Additive White and Pink Noises, Alshabrawy, O., Hassanien, A. 2014, Proceedings of the 5th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2014, Cham, Springer
  • Underdetermined blind separation of an unknown number of sources based on Fourier transform and Matrix Factorization, Alshabrawy, O., Ghoneim, M., Salama, A., Hassanien, A. 1 Dec 2013, 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, Piscataway, Institute of Electrical and Electronics Engineers Inc.
  • Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization, Alshabrawy, O., Ghoneim, M., Awad, W., Hassanien, A. 1 Dec 2012, 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012, Piscataway, Institute of Electrical and Electronics Engineers Inc.

  • Alaa Elnabawy Biomarkers identification framework using causal discriminative analysis Start Date: 01/12/2022

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