Deutsch Intern
    Chair of Computer Science VI - Artificial Intelligence and Applied Computer Science

    Amar Hekalo, M. Sc.

    University of Würzburg
    Chair of Artificial Intelligence
    and Knowledge Systems
    Am Hubland
    D-97074 Würzburg

    Room:  B001
    Phone: +49 931 / 31 - 86385

    amar.hekalo@uni-wuerzburg.de

    About Me

    After completing my master's degree in physics, I joined the Chair of Artificial Intelligence and Knowledge Systems in November 2018. Since then, I have been researching, among others with the Uniklink Würzburg, to improve and make documentation in medicine more efficient with the help of language and image processing via Deep Learning and rule-based methods.

     

    Research Interests

    My research interests are especially in automated image processing and NLP. This includes in particular methods of information extraction, image classification, image segmentation as well as object recognition, specifically latest architectures, improvement of training (e.g. data augmentation and hyperparameter optimization) as well as medical applications.

    Currently I am looking for HiWis with experience in frontend with Angular and backend with NodeJS to support me in the development of a documentation and e-learning tool for radiology. If you are interested, you can simply reach me by mail.

    Projects

    I am also a member of the ESF-ZDEX ("Center for Digital Experimentation") project, which aims to bring current research topics closer to SMEs in the Lower Franconia region. For this purpose, we are in constant exchange with our cooperation partners in the form of regular meetings, projects and events. New cooperation partners are always welcome.

     

    Teaching

    WS 21 Software Practical Course (Supervisor for Topics)
    Since SS 20 Programming with Neural Networks (Lecture and exercise)
    SS19 Software Engineering (Exercise)
    Since SS 19 Seminar "Current Trends in Artificial Intelligence“ (Topics)
    Since WS 18/19 Artificial Intelligence 2 (Exercise)
       

    Publications

    • Endoscopic Detection and Segmentation of Gastroenterological Diseases with Deep Convolutional Neural Networks. Krenzer, Adrian; Hekalo, Amar; Puppe, Frank. Vol. 2595, S. Ali; C. Daul; J. Rittscher; D. Stoyanov; E. Grisan (eds.). CEUR-WS, 2020.