Adewale Akinfaderin, D.Eng.

Adewale Akinfaderin

Adewale Akinfaderin, D.Eng.

Department: Artificial Intelligence & Machine Learning


Dr. Adewale Akinfaderin is an Adjunct Professor for the Online Engineering Programs at George Washington University in Washington, DC. His expertise lies in reproducible and end-to-end AI methods, practical implementations, and aiding global healthcare companies in formulating and developing scalable solutions for interdisciplinary AI challenges. With over a decade of experience, he has a strong track record of scientific publications and significant collaborative work. Dr. Akinfaderin has presented at prominent technical conferences. He has also contributed to the Program Committee for the 2019 and 2020 Conference on Neural Information Processing Systems (NeurIPS) - Workshop on Machine Learning for the Developing World, and played a role in the Ethics Advisory Board and Review Committee for the Association of Computational Linguistics (ACL-IJCNLP 2021). He is also a reviewer for major machine learning and data science conferences.

Dr. Akinfaderin holds a Doctor of Engineering degree from The George Washington University, two Master of Science degrees in Theoretical Nuclear Physics and Experimental Physics from North Carolina Central University and Florida State University, and a Bachelor of Science degree in Applied Physics from the University of Lagos.

  • Adelani, D. I., Abbott, J., Neubig, G., D’souza, D., Kreutzer, J., Lignos, C., ..., Akinfaderin, A., … & Osei, S. (2021). MasakhaNER: Named entity recognition for African languages. Transactions of the Association for Computational Linguistics, 9, 1116-1131.
  • Akinfaderin, A. (2020). HausaMT v1. 0: Towards English–Hausa Neural Machine Translation. In Proceedings of the The Fourth Widening Natural Language Processing Workshop (pp. 144-147).
  • Nekoto, W., Marivate, V., Matsila, T., Fasubaa, T., Fagbohungbe, T., Akinola, S. O., ..., Akinfaderin, A., … & Bashir, A. (2020). Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 2144-2160).
  • Akinfaderin, A., & Wahab, O. (2019). NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term Memory. arXiv preprint arXiv:1910.04865.