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Mr. Anas Tahir

Research Assistant




Academic Background

MSc of Science in Electrical Engineering

Research Interests:

Artifficial Intelligence, Machine Learning, Image Processing, Signal Processing, Pattern Recognition, Data Mining, and Biomedical Engineering

A Bit About Me

ANAS M. TAHIR received the B.Sc. and M.Sc. degree in electrical engineering from Qatar University in 2018 and 2021, respectively. He worked as a graduate teaching and research assistant at QU from October 2018 till July 2020. Since August 2020 he has been working as a research assistant at QU on several research grants supported by Qatar National Research Fund. He has been extensively working on biomedical computer vision and clinical signal-related problems for developing computer-aided diagnostic systems. He managed to publish three articles as the first author in top-tier journals. Besides, he contributed significantly to several other journal papers.

Together with his teammates in QU research team and in collaboration with cardiology consultants from HMC, they proposed a novel approach for real-time R-peak detection in low-quality Holter ECGs. They published two journal papers in IEEE Transaction of Biomedical Engineering and IEEE Transaction of Neural Networks, two of the top journals in the domain. Recently, they submitted a patent for a novel approach for ECG denoising using Generative Adversarial Network (GAN). Their work will help to produce an end-to-end solution for ECG denoising, R-peak detection, and reliable arrhythmia detection from Holter ECG signals. 

In collaboration with radiologists from HMC, Anas and his teammates created, COVID-QU-Ex, the largest benchmark COVID-19 chest X-ray dataset with ground-truth lung segmentation masks using a collaborative-human machine approach. They won 2nd place in the Artificial Intelligence National Competition, Qatar 2021. They managed to provide a real-time online solution for localization, quantification, and severity grading of COVID-19 Infections from chest X-rays.

Currently, Anas is working on a project to develop a smart system to assess and recommend the best bioprosthetic heart valve designs for transcatheter aortic valve replacement surgery.

Let's Get


  • ResearchGate
  • Google Scholar
  • LinkedIn
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