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Zubair Afzal (1979, Pakistan) attained his Masters degree in Software Systems Engineering with a specialization in Information Systems, Data Mining, and Data Exploration in 2006 from Aachen University of Technology, RWTH-Aachen, Germany. His research thesis "Retrievel of spoken documents using fuzzy keyword search in syllable transcripts" was carried out at Fraunhofer Institute of Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany in collaboration with Deutsche Welle and Westdeutschen Rundfunks (WDR). In September 2006 he joined the subgroup of Discrete Algebra and Geometry of the Department of Mathematics and Computer Science at the Eindhoven University of Technology, The Netherlands as a software developer where he was involved in developing and incorporating interactive mathematics applications to learning management systems such as Moodle. In 2007, he joined Software Technology program of 3TU School for Technological Design and attained a Professional Doctorate in Engineeging (PDEng) degree from the Eindhoven University of Technology, The Netherlands. During his PDEng, he carried out several industrial projects of companies like ViNotion, NXP Semiconductors, FEI Company, CERN, and TechUnited. From December 2008 till September 2009 he worked at Philips Healthcare on designing and developing a prototype application using Microsoft PixelSense (formerly called Microsoft Surface table) that enables enhanced collaborations between clinicians during Tumor Board meetings.

• Generating and evaluating a propensity model using textual features from electronic medical records
• Concept recognition in French biomedical text using automatic translation
• Erasmus MC at CLEF eHealth 2016: Concept recognition and coding in French texts
• Extraction of chemical-induced diseases using prior knowledge and textual information
• Chemical entity recognition in patents by combining dictionary-based and statistical approaches
• RELigator: Chemical-disease relation extraction using prior knowledge and textual information
• Patent mining: combining dictionary-based and machine-learning approaches
• Biomedical Concept Recognition in French Text Using Automatic Translation of English Terms
• Knowledge-based extraction of adverse drug events from biomedical text
• ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus