Aymane Ahajjam
- Research Engineer II, CEM Research Institute
Contact Info
- Email: mohamed.ahajjam@UND.edu
- Alt Email: aymane.ahajjam@gmail.com
- Office: 445.216.1297
- Alt Phone: 445.216.1297
Office Address
Upson II Room 366D
243 Centennial Drive Stop 8155
Grand Forks, ND 58202-8155
Biography
Aymane Ahajjam is a Research Engineer II at the University of North Dakota's Department of Electrical Engineering and Computer Science (SEECS). He earned his PhD in Data Science from ENSIAS at Mohammed V University in Rabat and the International University of Rabat (UIR). His research primarily focuses on leveraging Artificial Intelligence to solve problems in energy consumption monitoring and environmental sciences. Throughout his research journey, Aymane has contributed to various interdisciplinary projects, bringing expertise in data analysis, machine learning, and geospatial modeling. He has also taken on key responsibilities such as mentoring students, teaching, and grant writing, fostering a collaborative research environment.
Data science, Remote sensing, Data wrangling, Machine learning, Deep learning, Time series forecasting
Ahajjam, A., Allgaier, M., Chance, R., Chukwuemeka, E., Putkonen, J., & Pasch, T. (2024). Enhancing prediction of Wildfire Occurrence and Behavior in Alaska using Spatio-Temporal Clustering and Ensemble Machine Learning. Ecological Informatics, 102963.
Chance, R., Ahajjam, A., Putkonen, J., & Pasch, T. (2024). Artificial intelligence for predicting arctic permafrost and active layer temperatures along the Alaskan North Slope. Earth Science Informatics, 1-19.
Ahajjam, A., Putkonen, J., Chukwuemeka, E., Chance, R., & Pasch, T. J. (2024). Predictive Analytics Of Air Temperature In Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition And Deep Learning. Forecasting, 6(1), 55–80.
Ahajjam, A., Putkonen, J., Pasch, T. J., & Zhu, X. (2023). Short-And Mid-Term Forecasting Of Pan-Arctic Sea Ice Volume Using Variational Mode Decomposition And Bidirectional Long Short-Term Memory. Geosciences, 13(12), 370.
Patterson, H., Putkonen, J., Chance, R., Ahajjam, A., & Pasch, T. (2023). GIS Analyses For Identifying Permafrost Risks; Alaska, USA. AGU23.
Ahajjam, A., Chance, R., Patterson, H., Putkonen, J., & Pasch, T. (2023). Automatic Detection And Classification Of Infrastructure In Alaska Using Deep Learning And Satellite Imagery. AGU Fall Meeting Abstracts, 2023, C33D-143.
Putkonen, J., Aymane Ahajjam, M., Pasch, T., & Chance, R. (2023). A Hybrid VMD-WT-Inceptiontime Model For Multi-Horizon Short-Term Air Temperature Forecasting In Alaska. EGU General Assembly Conference Abstracts, EGU-10726.
Chance, R., Ahajjam, A., Putkonen, J., & Pasch, T. (2023). Machine Learning For Prediction Of Permafrost And Active Layer Temperatures Along The North Slope Of Alaska. AGU Fall Meeting Abstracts, 2023, C53C-1055.
Putkonen, J., Chance, R., Pasch, T., Patterson, H., & Ahajjam, A. (2023). Future Changes In Permafrost Regime; Alaska, USA. AGU Fall Meeting Abstracts, 2023, C52B-06.
Zhu, X., Pasch, T. J., Ahajjam, M. A., & Bergstrom, A. (2022). Environmental Monitoring For Arctic Resiliency And Sustainability: An Integrated Approach With Topic Modeling And Network Analysis. Sustainability, 14(24), 16493.
Khoei, T. Talaei, Gasimova, A., Ahajjam, M. A., Al Shamaileh, K., Devabhaktuni, V., & Kaabouch, N. (2022). A Comparative Analysis Of Supervised And Unsupervised Models For Detecting Gps Spoofing Attack On Uavs. 2022 IEEE International Conference On Electro Information Technology (Eit), 279–284. IEEE.
Khoei, Tala Talaei, Ahajjam, M. A., Hu, W. C., & Kaabouch, N. (2022). A Deep Learning Multi-Task Approach For The Detection Of Alzheimer’s Disease In A Longitudinal Study. 2022 IEEE International Conference On Electro Information Technology (Eit), 315–319. IEEE.
Ahajjam, M. A., Licea, D. B., Ghogho, M., & Kobbane, A. (2022). Experimental Investigation Of Variational Mode Decomposition And Deep Learning For Short-Term Multi-Horizon Residential Electric Load Forecasting. Applied Energy, 326, 119963.
Ahajjam, M. A., Essayeh, C., Ghogho, M., & Kobbane, A. (2021). On Multi-Label Classification For Non-Intrusive Load Identification Using Low Sampling Frequency Datasets. 2021 IEEE International Instrumentation And Measurement Technology Conference (I2MTC), 1–6. IEEE.
Essayeh, C., Raiss El-Fenni, M., Dahmouni, H., & Ahajjam, M. A. (2021). Energy Management Strategies For Smart Green Microgrid Systems: A Systematic Literature Review. Journal Of Electrical And Computer Engineering, 2021(1), 6675975.
Ahajjam, M. A., Licea, D. B., Ghogho, M., & Kobbane, A. (2020). Electric Power Quality Disturbances Classification Based On Temporal-Spectral Images And Deep Convolutional Neural Networks. 2020 International Wireless Communications And Mobile Computing (IWCMC), 1701–1706. IEEE.
Ahajjam, M. A., Bonilla Licea, D., Essayeh, C., Ghogho, M., & Kobbane, A. (2020). MORED: A Moroccan Buildings’ Electricity Consumption Dataset. Energies, 13(24), 6737.
Ahajjam, M. A., Bonilla Licea, D., Ghogho, M., & Kobbane, A. (2020). IMPEC: An Integrated System For Monitoring And Processing Electricity Consumption In Buildings. Sensors, 20(4), 1048.
Zakroum, M., Ghogho, M., Faqir, M., & Ahajjam, A. (2017). Deep Learning For Inferring The Surface Solar Irradiance From Sky Imagery. 2017 International Renewable And Sustainable Energy Conference (IRSEC), 1–4. IEEE.
- Ph.D in Computer Science, International University of Rabat, Morocco.
- M.S in Mechatronics Engineering, Ecole Nationale des Sciences Appliquées de Tétouan (5-year program), Morocco.