Biography

Prof. Mohamed Medhat Gaber is the Founder and Director of Gaber Infinity Labs Ltd and an Adjunct Professor at Queensland University of Technology. Previously, Mohamed held the position of Professor in Data Analytics at the School of Computing and Digital Technology, Birmingham City University, from 2016 to 2025. During this time, he was seconded for three academic years as the founding Dean of the Faculty of Computer Science and Engineering at Galala University, where he played a pivotal role in establishing a world-class academic institution. Mohamed earned his PhD from Monash University, Australia, with a prestigious internship at IBM T. J. Watson Research Lab in New York, USA. His doctoral thesis was nominated for the Mollie Holman Award for Best PhD Theses in 2006, a testament to the exceptional quality of his research. Mohamed’s academic journey includes appointments at renowned institutions such as the University of Sydney, CSIRO, and Monash University in Australia. In the United Kingdom, he has contributed to the academic excellence of the University of Portsmouth, Robert Gordon University, and Birmingham City University. At Birmingham City University, he founded the Data Analytics and Artificial Intelligence (DAAI) research group, directed research at the School of Computing and Digital Technology, and led the Centre for Computing & Data Science, cementing his reputation as a leader in AI research and education.

Prof. Gaber’s research in Artificial Intelligence has been supported by prestigious funding bodies, including the Australian Research Council (ARC) and the EU Horizon 2020 program. His prolific academic output includes over 250 published papers, three co-authored monograph-style books, and seven edited or co-edited volumes on AI. His work has garnered over 13,100 citations, earning him an impressive h-index of 49. According to a recent study by Stanford University and Elsevier, Prof. Gaber ranks among the top 2% of the most cited scientists worldwide—a recognition of his profound impact on the global scientific community. His research interests span a wide array of AI domains, including ensemble learning, data stream analysis, medical image processing, natural language processing, time series classification, and deep learning. These contributions have not only advanced the field of AI but also addressed critical challenges across industries, from healthcare to financial services.
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As a dedicated mentor, Prof. Gaber has supervised or co-supervised 24 PhD students to successful completion and has served as an examiner for 51 PhD candidates. His commitment to nurturing the next generation of AI researchers is matched by his leadership within the global AI community. He has chaired numerous high-profile conferences, including serving as General Co-Chair of the 3rd IEEE International Conference on Data Science and Computational Intelligence (DSCI 2019) and Program Committee Co-Chair of the IEEE Mobile Data Management (MDM 2016), among many others. Prof. Gaber’s contributions to higher education and research have earned him the distinction of Fellow of the British Higher Education Academy (HEA). 

Publications (Selected Journal Articles)

Abbas A., Gaber M. M., and Abdelsamea M., CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification, IEEE Transactions on Emerging Topics in Computing, Volume 13, Issue 3, pp. 1043–1054, July–Sept. 2025, IEEE pres.

Chambers L., Gaber M. M., and Ghomeshi H., DeepStreamEnsemble: Streaming Adaptation to Concept Drift in Deep Neural Networks, International Journal of Machine Learning and Cybernetics, Volume 16, pp. 3955–3976, 2025, Springer.

​Chughtai S., Senousy Z., Mahany A., Elmitwally N., Ismail K. N., Gaber M. M., and Abdelsamea M. M., DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification, IEEE Open Journal of the Computer Society, Volume 5, pp. 380–388, 2024, IEEE Press

Hettiarachchi H., Adedoyin-Olowe M., Bhogal J., and Gaber M. M., TTL: Transformer-based Two-phase Transfer Learning for Cross-lingual News Event Detection, International Journal of Machine Learning and Cybernetics, Voume 14, pp. 2739—2760, 2023, Springer.

Hettiarachchi H., Adedoyin-Olowe M., Bhogal J., and Gaber M. M., WhatsUp: An Event Resolution Approach for Co-occurring Events in Social Media, Information Sciences, Volume 625, May 2023, pp. 553–577, Elsevier.

Chambers L., Gaber M. M., and Ghomeshi H., AdaDeepStream: Streaming Adaptation to Concept Evolution in Deep Neural Networks, Applied Intelligence, Volume 53, Issue 22, pp. 27323–27343, 2023, Springer.

Ragab Hassen H., Alabdeen Y. Z., Gaber M. M., and Sharma M., D2TS: A Dual Diversity Tree Selection Approach to Pruning of Random Forests, International Journal of Machine Learning and Cybernetics, Volume 14, Issue 2, pp. 467–481, 2023, Springer.

​Zidan U., El Desouky H., Gaber M. M., and Abdelsamea M. M., From Pixels to Deposits: Porphyry Mineralisation with Multispectral Convolutional Neural Networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 16, pp. 9474–9486, 2023, IEEE press. 

Zidan U., Gaber M. M., and Abdelsamea M. M., SwinCup: Cascaded Swin Transformer for Histopathological Structures Segmentation in Colorectal Cancer, Expert Systems with Applications, Volume 216, 15 April 2023, p. 119452, Elsevier.

Senousy Z., Gaber M. M., and Abdelsamea M. M., AUQantO: Actionable Uncertainty Quantification Optimization in Deep Learning Architectures for Medical Image Classification, Applied Soft Computing, Volume 146, October 2023, p. 110666, Elsevier.

​Senousy Z., Abdelsamea M. M., Gaber M. M., Abdar M., Acharya U. R., Khosravi A. and Nahavandi S., MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification, IEEE Transactions on Biomedical Engineering, Volume 69, Issue 2, pp. 818-829, 2022, IEEE press. 

​Hettiarachchi H., Adedoyin-Olowe M., Bhogal J., and Gaber M. M., Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media, Machine Learning, Volume 111, Issue 1, pp. 49-87, 2022, Springer. 

​Abbas A., Abdelsamea M., and Gaber M. M., 4S-DT: Self Supervised Super Sample Decomposition for Transfer Learning with Application to COVID-19 Detection, IEEE Transactions on Neural Networks and Learning Systems, Volume 32, Issue 7, pp. 2798-2808, July 2021. 

 Hatwell J., Gaber M. M., and Azad R. M. A., Ada-WHIPS: Explaining AdaBoost Classification with Applications in the Health Sciences, BMC Medical Informatics and Decision Making, Volume 20, Number 1:250, December 2020, Springer Nature.

Hatwell J., Gaber M. M., and Azad R. M. A., CHIRPS: Explaining Random Forest Classification, Artificial Intelligence Review, Volume 53, pp. 5747–5788, 2020, Springer. 

​Abdallah Z. S., and Gaber M. M., ​Co-eye: A Multi-resolution Ensemble Classifier for Symbolically Approximated Time Series, Machine Learning, Volume 109, pp. 2029–2061, 2020, Springer. 

Ghomeshi H., Gaber M. M., and Kovalchuk Y., A Non-Canonical Hybrid Metaheuristic Approach to Adaptive Data Stream Classification, Future Generation Computer Systems, Volume 102, January 2020, pp. 127–139, Elsevier

​Ghomeshi H., Gaber M. M., and Kovalchuk Y., ​EACD: Evolutionary Adaptation to Concept Drifts in Data Streams, Data Mining and Knowledge Discovery, May 2019, Volume 33, Issue 3, pp 663–694. Springer-Verlag. 

Elyan E., and Gaber M. M., A Genetic Algorithm Approach to Optimising Random Forests Applied to Class Engineered Data, Information Sciences, Volume 384, April 2017, pp. 220–234, Elsevier.

Le T., Stahl F., Gaber M. M., Gomes J. B., and Di Fatta G., On Expressiveness and Uncertainty Awareness in Rule-based Classification for Data Streams, Neurocomputing, Volume 265, 22 November 2017, pp. 127–141, Elsevier.

Adedoyin-Olowe M., Gaber M. M., Martin-Dancausa C., Stahl F., and Gomes J. B., A Rule Dynamics Approach to Event Detection in Twitter with Its Application to Sports and Politics, Expert Systems with Applications, Volume 55, 15 August 2016, pp. 351–360, Elsevier

Abdallah Z. S., Gaber M. M., Srinivasan B., and Krishnaswamy S., AnyNovel: Detection of Novel Concepts in Evolving Data Streams, Evolving Systems, June 2016, Volume 7, Issue 2, pp. 73–93, Springer.

Abdallah Z. S., Gaber M. M., Srinivasan B., and Krishnaswamy S., Adaptive Mobile Activity Recognition System with Evolving Data Stream, Neurocomputing, Volume 150, Part A, 20 February 2015, pp. 304–317, Elsevier. 

Abdelsamea M. M., Gnecco G., and Gaber M. M., An Efficient Self Organizing Active Contour Model for Image Segmentation, Neurocomputing, Volume 149, Part B, 3 February 2015, pp. 820–835, Elsevier.

​Gomes J. B., Gaber M. M., Menasalvas E., and Sousa P., Mining Recurring Concepts in a Dynamic Feature Space, IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Issue 1, pp. 95-110, January 2014. ​

Chong S. K., Gaber M. M., Krishnaswamy S., and Loke S. W., Energy Conservation in Wireless Sensor Networks: A Rule-based Approach, Knowledge and Information Systems (KAIS) Journal, Volume 28, Number 3, pp. 579–614, ISSN 0219-1377, Springer London, 2011

10 Selected Survey/Review Articles

  1. Farag H. O., Gaber M. M., Awad M. I., and Elhady N. E., Prosthetic Hands: A Review of Muscle Synergy, Machine Learning and Edge Computing, ACM Computing Surveys (CSUR), 2025, ACM press.
  2. Abdelsamea M., Zidan U., Senousy Z., Gaber M. M., Rakha E. Ilyas M., A Survey on Artificial Intelligence in Histopathology Image Analysis, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 12, Issue 6, 2022, Wiley press. [DOI]
  3. Dridi A., Gaber M. M., Azad R. M. A., and Bhogal J., Scholarly Data Mining: A Systematic Review of its Applications, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 11, Issue 2, March/April 2021, Wiley press. [DOI]
  4. Gaber M. M., Aneiba A., Basurra S., Batty O., Elmisery A., Kovalchuk Y., and Habib ur Rehman M. Internet of Things and Data Mining: From Applications to Techniques and Systems, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 9, Issue 3, May/June 2019, e12922019. [DOI] 
  5. Abdallah Z. S., Gaber M. M., Srinivasan B., and Krishnaswamy S., Activity Recognition with Evolving Data Streams: A Review, ACM Computing Surveys (CSUR), Volume 51 Issue 4, July 2018, ACM press. [DOI]
  6.  Hussein A., Gaber M. M., Elyan E., and Jayne C., Imitation Learning: A Survey of Learning Methods, ACM Computing Surveys (CSUR), Volume 50 Issue 2, April 2017, ACM press. [DOI] 
  7. Fawagreh K., Gaber M. M., and Elyan E., Random Forests: From Early Developments to Recent Advancements, Systems Science & Control Engineering, Volume 2, Issue 1, 2014, pp. 602-609, Taylor & Francis. [DOI]
  8. Gaber M. M., Gama J., Krishnaswamy S., Gomes J., and Stahl F., Data Stream Mining in Ubiquitous Environments: State-of-the-art and Current Directions, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 4, Issue 2, pp. 116-138, March/April 2014. [DOI]
  9. ​Gaber M. M., Advances in Data Stream Mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 2, Issue 1, pp. 79-85, 2012. [DOI]
  10. Gaber, M, M., Zaslavsky, A., and Krishnaswamy, S., Mining Data Streams: A Review, ACM SIGMOD Record, Vol. 34, No. 1, pp. 18-26, June 2005, ISSN: 0163-5808. [DOI]

List of Publications (different sources)

Since March, 2014
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