I am currently Research Fellow at King's College London, UK.

Prior to joining KCL, I was Senior Research Engineer II at Imagination Technologies, UK. I worked there as part of the Vision and AI research group looking at designing, training, deploying and optimising neural networks for Imagination's Neural Network Accelerator products.

In 2017, I completed my PhD in Machine Learning (Computer Science) from City, University of London under the supervision of Greg Slabaugh. For my research, I contributed novel probabilistic regression methods to learn hand pose and orientation using uncalibrated colour images. At City, I also worked in close collaboration with my colleagues on a range of medical imaging problems.

My research interests include probabilistic regression, optimisations for neural networks, fully-convolutional neural networks and neural network compression.

Latest News


Jun 23, 2023 Code: Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation source code is now released at https://github.com/masadcv/MONet-MONAILabel

Jun 23, 2023 Publication: our paper Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation has been accepted at International Conference on Medical Image Computing and Computer Assisted Intervention 2023

Nov 23, 2022 Publication: our paper FastGeodis: Fast Generalised Geodesic Distance Transform has been accepted at Journal of Open Source Software 2022

Jul 1, 2022 Code: FastGeodis is released. It provides an efficient PyTorch implementation for computing Geodesic and Euclidean distance transforms on GPU (CUDA) and CPU (OpenMP) hardwares. Muhammad contributed to its implementation, documentation and release

Apr 21, 2022 Code: numpymaxflow is released. It provides max-flow/min-cut method for 2D images and 3D volumes in numpy. Muhammad contributed to its implementation and release

Apr 6, 2022 Code: torchmaxflow is released. It provides max-flow/min-cut method for 2D images and 3D volumes in PyTorch. Muhammad contributed to its implementation and release

Mar 1, 2022 Code: MONAI Label v0.3.2 is released. Muhammad contributed to implementing scribbles-based interactions for OHIF viewer in MONAI Label

Feb 28, 2022 Code: ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation source code is now released at https://github.com/masadcv/ECONet-MONAILabel

Feb 28, 2022 Publication: our paper ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation has been accepted at MIDL 2022

Sep 23, 2021 Code: MONAI Label v0.2 is released. Muhammad contributed to enabling scribbles-based interactive segmentation tools in MONAI Label. More info at MONAI Label Scribbles Wiki

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