About me

I come from computer science and software engineering backgrounds, and mainly interested in leveraging machine/deep learning in medical image analysis. My research interests in medical imaging began during my MSc Software Engineering degree at DePaul University 🇺🇸 where I worked on content based image retrieval (CBIR) systems and on bridging the gap between human and computer interpretation of similarity in medical imaging.This was followed by an internship at RWTH Aachen University 🇩🇪 where I worked on building a joint database for dermoscopic images that would serve as a standard reference for researchers. I then worked as a researcher at Clermont Auvergne University 🇫🇷 where I did research on liver lesion segmentation from MRI and medical ultrasound imaging using fuzzy logic based techniques.

I eventually earned my Ph.D in Computing Science from the University of Stirling 🇬🇧 where I worked on various topics that encompassed the medical image analysis standard pipeline, and were mainly geared towards melanoma detection in particular. That is, I developed algorithms pertaining image segmentation, feature extraction, and image classification, and have also developed statistical measures for extracting fine features from skin lesions, in addition to developing approaches for tackling the ambiguity inherent in medical images. You can download my Ph.D thesis from here.

Away from academia, I’m very passionate about productivity. You can check my FREE course How To Be Productive for some proven strategies I personally tried out that can improve your productivity dramatically!