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sahar.qadan@gju.edu.jo

Jun.-Prof. Dr. Sahar Qaadan

Theory of Machine Learning

sahar.qadan@gju.edu.jo

https://ees.uni-wuppertal.de/de/gastforscherin/qaadan/

I am working in Tobias Glasmachers group (theory of machine learning) at the Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany. My research interests are supervised machine learning and optimization.

Linkedin profile: https://www.linkedin.com/in/dr-ing-sahar-qaadan-81396321/

 

Since 2019: am a researcher in supervised machine learning.

2016-2019: Phd candidate in Tobias Glasmachers's group (Theory of machine learning) at the Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany. My research interests are supervised machine learning and optimization with (stochastic) gradient-based methods.

2015-2016: Research assistant and control engineer at the Institute of Neuroinformatik, Ruhr University Bochum, Germany. (Industrial project in the field of Autonomous Systems ADAF (for BMW) at NISYS GmbH in the technology center, Ruhr university Bochum).  

2014-2015: Research assistant and control engineer at the Institute of Neuroinformatik, Ruhr University Bochum, Germany. (Industrial project in thermal management control of Data center using Neural Network and PLC systems in the technology center, Ruhr university Bochum).                             

2014: Internship in (supervised) Machine Learning form Technical Calculation Department, Thyssenkrupp Rothe Erde in Lippstadt, Germany.  


2011-2014: Msc. in Automation and Robotics from the School of Electrical Engineering and Information Technology, Technical University Dortmund in Dortmund, Germany.


2008-2011: Teaching assistant and lab supervisor at the School of Applied Technical Sciences, German Jordanian University in Amman, Jordan.

2003-2008: Bsc. in Mechatronics engineering from the Faculty of Engineering and Technology, Universty of Jordan in Amman, Jordan. 

    2019

  • Dual SVM Training on a Budget
    Qaadan, S., Schüler, M., & Glasmachers, T.
    In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications
    link

    • 2018

  • Speeding Up Budgeted Dual SVM Training with Precomputed GSS
    Glasmachers, T., & Qaadan, S.
    (M. M. -y-G. Ruben Vera-Rodriguez Sergio Velastin & Morales, A., Eds.), The 23rd Iberoamerican Congress on Pattern Recognition
  • Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search
    Glasmachers, T., & Qaadan, S.
    In G. Nicosia, Pardalos, P., Giuffrida, G., Umeton, R., & Sciacca, V. (Eds.), The 4th International Conference on machine Learning, Optimization and Data science - LOD 2018
    link
  • Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
    Qaadan, S., & Glasmachers, T.
    13th WiML Workshop, Co-located with NeurIPS, Montreal, QC, Canada
  • Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
    Qaadan, S., & Glasmachers, T.
    arXiv.org
    link
  • Multi-Merge Budget Maintenance for Stochastic Coordinate Ascent SVM Training
    Qaadan, S., & Glasmachers, T.
    Artificial Intelligence International Conference – A2IC 2018
Ruhr University Bochum, Bochum, Germany:

2018: Informatik 1 https://www.ei.rub.de/studium/lehrveranstaltungen/634/

2016: Selected topics in neurocomputing: Intelligence

German Jordanian University, Amman, Jordan:

2008-2011: Lab supervisor in automatic control system, programmable logic controllers (PLC), automation systems and smart buildings, hydraulics and pneumatics, manual drawing, AUTOCAD.

Budget Accelerated Coordinate Descent with Adaptive Coordinate Frequencies Large-scale SVM Training.

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculties of Computer Science and Medicine at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de