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Ahmed Hafez

Early Stage Researcher
+34 963 543 269
Universidad de Valencia
C/ Catedrático José Beltrán Nº 2
46980 Paterna Valencia
Integrative genomics/proteomics of yeast infections

I studied computer science at the faculty of Computers and Information (FCI), Minia University. In 2008 I received my B.Sc. degree in Computer Science. After that I worked at FCI-Minia University as teacher assistant. I continued my master studies in FCI- Cairo University. My work was in social network analysis supervised by (Prof. Aly Fahmy and Prof. Aboul Ella Hassanien). My focus was in community detection (CD) in social network. Community detection in such network has a wide range of applications. For example, Facebook and other companies use community detection along with other social network analysis methods to analysis their data to suggest friends, products and movies to their users.  At the end of 2014 I got my master degree M.Sc from FCI- Cairo University. After my master, I continued my research as a member of Scientific Research Group in Egypt (SRGE). My main research interests are in the areas of bioinformatics, social networks analysis, computer vision, machine learning, data mining, and parallel processing.

In 2015 I applied for ESR position in Opathy training network and I got accepted. I start working in Biotechvana in april 2016. I am working on subproject of Opathy project title "Integrative genomics/proteomics of yeast infections" supervised by Dr Carlos Llorens and Dr Toni Gabaldon. My project objectives are to develop a bioinformatics infrastructure including database (the Opathy database), scripting tools for pipeline-workflow automatization, friendly-to-use interfaces and controlling algorithms for efficient processing, analysis and storage of omic fungal data and their infection metadata. This includes pipeline tools for data analysis and knowledge discovery based on the data interrogation through the development of state of the art computational tools for automatization of primer design, analysis of collected data as well as algorithms for identification of potential biomarkers candidates that could be able to determine the infection stage, the infective pathogen, and the potential resistance traits.