Muhammad Moazam Fraz
Tenured Associate Professor
Department of Computing
School of Electrical Engineering and Computer Science (SEECS)
National University of Sciences and Technology (NUST)
NUST Campus H-12, Islamabad
Computer Vision, Deep Learning, Computational Pathology, Medical Image Analysis, Visual surveillance, Scene / Semantic understanding
PhD - Computer Science - (Medical Image Analysis/Computer Vision) - Kingston University London - United Kingdom - 2013
Dr. Muhammad Moazam Fraz completed his PhD (Computer Science) from Faculty of Science Engineering and Computing, Kingston University London in 2013. His research area was application of machine learning / computer vision techniques for diagnostic retinal image analysis. His PhD thesis was nominated for IET excellence awards 2013. After completing his PhD, he has worked as post doc researcher at Kingston University in collaboration with St George’s University of London and UK BioBank on the development of software tools for the epidemiologists to analyze the association between systemic and cardiovascular disease and the retinal vessel characteristics as potential biomarker. Afterwards, he joined NUST SEECS as Assistant Professor in June 2014.
Dr. Fraz received his MS and BS degrees in Software Engineering in 2008 and 2003 respectively. He was the recipient of two Gold Medals for 'Best Graduate Award' and 'Securing Top Position in the batch'. He has started his career in 2003 as a Software Development Engineer at Elixir Technologies Corporation, a California based software Company. He has served with Elixir until 2010 at various roles and capacities including software developer, development manager and program manager. He is a PMI (www.pmi.org) certified Project Management Professional (PMP).
He was awarded with Rutherford Visiting Fellowship by The Alan Turing Institute United Kingdom (UK's National Center for Data Science and Artificial Intelligence) to work as Senior Research Fellow at the Tissue Image Analytics (TIA) Lab, University of Warwick, UK in 2018, where he has worked on Deep learning-based histopathological analytics for better cancer grading and prognostication.
He was Program Committee member of MICCAI, MIUA, BMVC, ECCV and FIT Conferences. Besides, he was TPC co-chair of HONET 2015, CSDiFO 2016 and HONET 2020.
He is interested in using Computer Vision, Artificial Intelligence and Machine Learning to solve real world problems. Having more than 12 years of experience of working in industry and academia, Continually drive research and technology innovation to develop cutting-edge AI algorithms and methods with a special emphasis on designing deep learning architectures for solving various computer vision problems in academia and industry.
Specialization: Deep Learning, Artificial Intelligence, Computer Vision and Data Science; with application areas in Health-care (Computational pathology, Medical image analysis), Visual Surveillance in context of public security, and learning algorithms for semantic understanding of visual scene.
- Computer Vision, Deep Learning, Medical Image Analysis, Computational Pathology, Visual Surveillance, Human Activity Recognition, Scene / Semantic understanding, Data Science and Cognitive Computing
June 2013 - June 2014
March 2018 - Present
March 2018 - March 2019
Cources Taught at NUST SEECS