Mohamed Farag

Doctoral student at the University of Bonn specializing in developing methods for uncertainty estimation and explainability in Deep Learning models for Crops monitoring. Experience in Deep Learning across diverse fields, including Medicine. Eager to learn and expand horizons. Committed to developing skills in Reliable Deep Learning systems. Thrives in both autonomous and collaborative environments, excelling in independent work and contributing effectively to group projects with enthusiasm and energy.

• Farag, M., Emam, A., Leonhardt, J., Roscher, R. (2025). Enhancing Decision Support in Crop Production: Analyzing Conformal Prediction for Uncertainty Quantification. Computers and Electronics in Agriculture, 237, Part B, 110559
• Emam, A., Farag, M., Kierdorf, J., Klingbeil, L., Rascher, U., Roscher, R. (2025). A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning. In: Del Bue, A., Canton, C., Pont-Tuset, J., Tommasi, T. (eds) Computer Vision – ECCV 2024 Workshops. ECCV 2024. Lecture Notes in Computer Science, vol 15625. Springer
• A. Emam, M. Farag and R. Roscher, 'Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness,' in IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 8500505, doi: 10.1109/LGRS.2024.3365196.
• Farag, Mohamed and Kierdorf, Jana and Roscher, Ribana, 'Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods', in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 651-659, 2023.
• M. M. Farag, M. Fouad and A. T. Abdel-Hamid, 'Automatic Severity Classification of Diabetic Retinopathy Based on DenseNet and Convolutional Block Attention Module,' in IEEE Access, vol. 10.

Prof. Dr.-Ing. Ribana Roscher

• Head of the Data Science for Crop Systems Group at Forschungszentrum Jülich and Professor of Data Science for Crop Systems at University of Bonn
• mail: ribana.roscher@uni-bonn.de

Dr. Mariam Mohamed

• Machine Learning Researcher, Noah Labs
• mail: mariam.m.fouad13@gmail.com

Prof. Michael Goetz

• Junior-Professor at Ulm University
• mail: michael.goetz@uni-ulm.de

Assoc.Prof. Amr Talaat

• Vice Dean of Student Affairs
• mail: amr.talaat@guc.edu.eg

Prof. Yasser Hegazy

• President of the German University of Cairo
• mail: yasser.higazi@guc.edu.eg

• Farag, M., Emam, A., Leonhardt, J., Roscher, R. (2025). Enhancing Decision Support in Crop Production: Analyzing Conformal Prediction for Uncertainty Quantification. Computers and Electronics in Agriculture, 237, Part B, 110559
• Emam, A., Farag, M., Kierdorf, J., Klingbeil, L., Rascher, U., Roscher, R. (2025). A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning. In: Del Bue, A., Canton, C., Pont-Tuset, J., Tommasi, T. (eds) Computer Vision – ECCV 2024 Workshops. ECCV 2024. Lecture Notes in Computer Science, vol 15625. Springer
• A. Emam, M. Farag and R. Roscher, 'Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness,' in IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 8500505, doi: 10.1109/LGRS.2024.3365196.
• Farag, Mohamed and Kierdorf, Jana and Roscher, Ribana, 'Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods', in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 651-659, 2023.
• M. M. Farag, M. Fouad and A. T. Abdel-Hamid, 'Automatic Severity Classification of Diabetic Retinopathy Based on DenseNet and Convolutional Block Attention Module,' in IEEE Access, vol. 10.