Abbas Cheddad

Abbas Cheddad

Universitetslektor/docent

abbas.cheddad@bth.se

Institutionen för datavetenskap, Rum J3112

0455-385863

Personal Website

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CV

Short Biography

I did my PhD at the faculty of Computing and Engineering at the University of Ulster in Northern Ireland, UK. I worked as a post-doc at Umeå University (Sweden) and also was affiliated with the Karolinska Institute in Stockholm for several years. From Oct 2015, I joined the Blekinge Institute of Technology in Karlskrona (Sweden) as a senior lecturer (Dpt of Computer Science and Engineering) where I hold, currently, the title of  Associate Professor (Docent). Alongside my teaching duties, I am participating in a large research project, “Scalable resource-efficient systems for big data analytics,” where close research collaboration with industry is key for the project execution. I invest my expertise in image processing into this project that focuses on Big Data https://a.bth.se/bigdata/. More specifically, I am leading research theme B: Big data analytics for image processing. The research profile, Scalable resource-efficient systems for big data analytics, combines existing expertise in machine learning, data mining, and computer engineering to create new knowledge in the area of scalable resource-efficient systems for big data analytics. The value of the new knowledge is demonstrated and evaluated in two application areas (decision support systems and image processing). Currently, the group is collaborating, research-wise, with two companies, namely SONY Mobile (Lund) and ArkivDigital (Stockholm), by addressing practical industrial problems. I am also collaborating with GKN Aerospace Sweden AB (the world’s leading multi-technology tier 1 aerospace supplier). My research interests fall into the following disciplines: Computer vision, 3D reconstruction and Optical projection tomography, Steganography, Medical image analysis, Quantitative imaging bio-markers, Pattern localisation and recognition, Characterization and validation of imaging bio-markers, Evaluation of the association between image-based phenotypes and genomic biomarkers, Algorithms for the computer-guided analysis of multi-dimensional microscopy-data sets, Computational support for tissue-related target/ biomarker discovery, development and analysis, and machine learning applications. More info is provided in my CV.

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