Meet the team

EcRoFit Team

Mohamed Gaber

Mohamed Gaber

Professor in Data Analytics

School of Computing and Digital Technology

Mohamed is Professor in Data Analytics, the Centre Lead of the Centre of Enterprise Systems and the leader of the Data Analytics and Artificial Intelligence (DAAI) Research Group. He has published over 150 papers, co-authored two books, and edited/co-edited six books on machine learning and data mining. His research has attracted well over three thousand citations. In 2007, he was awarded the CSIRO Teamwork award. He organised/co-organised over 25 scientific events, including co-chairing the programme committee for the 17th International IEEE Mobile Data Management (MDM 2016).

Area of expertise:

  • Data mining
  • Machine learning
  • Data stream processing
  • Random forests
  • Data science
  • Data analytics for IoT applications
  • Mobile data mining

Mohamed Gaber is a Professor in Big Data Analytics at our School of Computing and Digital Technology.

He received his PhD from Monash University in Australia, and then held appointments with the University of Sydney and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). Prior to joining Birmingham City University, Mohamed held academic positions at Robert Gordon University and the University of Portsmouth.

  • PhD in Artificial Intelligence (Monash University)
  • MSc in Computer Science (University of Louisville)
  • Data analysis (postgraduate)
  • Data mining (postgraduate)
  • Machine learning (deep learning and ensemble learning)
  • Mobile and embedded data mining (Internet of Things applications)
  • Data stream mining (concept drift and resource-awareness)
  • Knowledge engineering (ontology learning)
  • Text mining (sentiment analysis
Gaber, M. (2017). Expressive modelling for trusted big data analytics: techniques and applications in sentiment analysis Gaber, M. (2016). A rule dynamics approach to event detection in Twitter with its application to sports and politics Gaber, M. (2016). An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests, Engineering Applications of Neural Networks: 17th International Conference, Aberdeen, Scotland Gaber, M. (2016). AnyNovel: detection of novel concepts in evolving data streams Gaber, M. (2016). Clustering-Based Spatio-Temporal Analysis of Big Atmospheric Data, International Conference on Internet of Things and Cloud Computing, Birmingham, UK Gaber, M. (2015). Spatio-temporal analysis of greenhouse gas data via clustering techniques, IEEE International Conference on Computer Supported Cooperative Work in Design CSCWD Gaber, M. (2015). Unsupervised learning techniques to diversifying and pruning random firest, RWTH Aachen University Gaber, M. (2015). A scalable expressive ensemble learning using random prism: A MapReduce approach Gaber, M. (2015). Advances in Social Media Analysis, Springer, New York, USA. Gaber, M. (2015). An efficient self-organising active contour model for image segmentation Gaber, M. (2015). An outlier detection-based tree selection approach to extreme pruning of random forests Gaber, M. (2015). Autonomic Discovery of News Evolvement in Twitter Gaber, M. (2015). Distributed Classification of Data Streams: An Adaptive Technique Gaber, M. (2015). Mobile data stream mining (advances), Big Data Meets Machine Learning 2015 Summer Training Workshop Gaber, M. (2015). Research and Development in Intelligent Systems XXXII: Incorporating Applications and Innovations in Intelligent Systems Full lists of publications are available at:

Selected publications

Authored books

  • Gaber M. M., Stahl F., and Gomes J., Pocket Data Mining: Big Data on Small Devices, Studies in Big Data Series, Volume 2, Springer Verlag, ISBN 978-3-319-02711-1, 2014.
  • Edwards K. J., and Gaber M. M., Astronomy and Big Data: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology, Studies in Big Data Series, Volume 6, Springer Verlag, ISBN 978-3-319-06599-1, 2014.
Edited Books
  • Gaber M. M., Cocea M., Wiratunga N., and Goker A. (Eds.), Advances in Social Media Analysis, Studies in Computational Intelligence, Vol. 602, Springer Verlag, ISBN 978-3-319-18457-9, 2015.
  • Sakr S., and Gaber M. M. (Eds.), Large Scale and Big Data: Processing and Management, Auerbach Publications, CRC Press, ISBN-10: 1466581506, ISBN-13: 978-1466581500, 2014.
  • Gaber M. M. (Ed.), Journeys to Data Mining: Experiences from 15 Renowned Researchers, a book published by Springer Verlag, ISBN 978-3-642-28046-7, 2012.
  • Gaber M. M. (Ed.), Scientific Data Mining and Knowledge Discovery: Principles and Foundations, a book published by Springer Verlag, ISBN 978-3-642-02787-1, 2009.
  • Ganguly A., Gama J., Omitaomu O., Gaber M. M., and Vatsavai R. R. (Eds.), Knowledge Discovery from Sensor Data, a book published by CRC Press, ISBN 1420082329, 9781420082326, 2008.
  • Gama J., and Gaber M. M. (Eds.), Learning from Data Streams: Processing Techniques in Sensor Networks, a book published by Springer Verlag, ISBN 978-3-540-73678-3, 2007.

Selected Journal Articles