Dr. Deogratias Mzurikwao

Deogratias has received his Ph.D. from the University of Kent on the Applications of Artificial Intelligence in healthcare in 2020. He previously did his master’s in AI at Tianjin University, in China. Deogratias has been researching and practicing machine learning, deep learning, image and voice signal processing which are the core elements of Artificial Intelligence, since 2012. During his time in China, he also worked for GTA, a big data analytic company based in Shenzhen, China as a data scientist. The company recruited him while he was still doing his master’s degree. While in China, Deogratias and his friend, formed a team that became second runners in the international innovation competition for automation and UAV in 2014. In 2019, he was among the 15 AI researchers who received the IEEE Outstanding Young Investigator Research Visit award to Sapienza University, Italy. His research interests are in the application of deep learning, machine learning, data analysis, and including explainable AI systems, super resolution, virtual and augmented reality. He has published several papers in peer-reviewed journals including Nature and IEEE. He has a total of 2653 citations, 11 h-Index and 13 i10-Index. Some of his published work has featured in global media houses including Forbes and the BBC. Deogratias has conducted AI based consultation works for the UNDP, UNICEF, UNU and the Tanzania ministry of health. Deogratias has received a grant from Google to develop an AI based screening tool for breast cancer, a grant from the Lacuna fund to develop machine learning ready dataset for predictions of Rabies outbreak. Recently, Dr Deogratias has won a grant from the IDRC to develop AI algorithm for screening of TB among people living with HIV. He is currently leading the Emerging Technologies for Health Research and Development laboratory at MUHAS (ETH) which utilises emerging technologies like AI, VR, AR and Blockchain technologies to develop healthcare solutions. Deogratias is currently serving as a secretariate member for the Lacuna fund.

List of Key publications

1. Mahmood, M., Mzurikwao, D., Kim, Y.S., Lee, Y., Mishra, S., Herbert, R., Duarte, A., Ang, C.S. and Yeo, W.H., 2019. Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithms. Nature Machine Intelligence, 1(9), pp.412-422.
2. Samuel, O.W., Asogbon, M.G., Geng, Y., Jiang, N., Mzurikwao, D., Zheng, Y., Wong, K.K., Vollero, L. and Li, G., 2021. Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems. Neural Computing and Applications, 33, pp.4793-4806.
3. Samuel, O.W., Yang, B., Geng, Y., Asogbon, M.G., Pirbhulal, S., Mzurikwao, D., Idowu, O.P., Ogundele, T.J., Li, X., Chen, S. and Naik, G.R., 2020. A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks. Future Generation Computer Systems, 110, pp.781-794.
4. Mzurikwao, D., Samuel, O.W., Asogbon, M.G., Li, X., Li, G., Yeo, W.H., Efstratiou, C. and Ang, C.S., 2019, June. A channel selection approach based on convolutional neural network for multi-channel EEG motor imagery decoding. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)(pp. 195-202). IEEE.
5. Sukums, F., Mzurikwao, D., Sabas, D., Chaula, R., Mbuke, J., Kabika, T., Kaswija, J., Ngowi, B., Noll, J., Winkler, A.S. and Andersson, S.W., 2023. The use of artificial intelligence-based innovations in the health sector in Tanzania: A scoping review. Health Policy and Technology, p.100728.
6. Mzurikwao, D., Ang, C.S., Samuel, O.W., Asogbon, M.G., Li, X. and Li, G., 2018, October. Efficient channel selection approach for motor imaginary classification based on convolutional neural network. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS)(pp. 418-421). IEEE.
7. Farlow, A., Hoffmann, A., Tadesse, G.A., Mzurikwao, D., Beyer, R., Akogo, D., Weicken, E., Matika, T., Nweje, M.I., Wamae, W. and Arts, S., 2023. Rethinking global digital health and AI-for-health innovation challenges. PLOS Global Public Health, 3(4), p.e0001844.
8. Asogbon, M.G., Samuel, O.W., Geng, Y., Chen, S., Mzurikwao, D., Fang, P. and Li, G., 2018, October. Effect of window conditioning parameters on the classification performance and stability of EMG-based feature extraction methods. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS)(pp. 576-580). IEEE.
9. Asogbon, M.G., Samuel, O.W., Nsugbe, E., Li, Y., Kulwa, F., Mzurikwao, D., Chen, S. and Li, G., 2023. Ascertaining the optimal myoelectric signal recording duration for pattern recognition-based prostheses control. Frontiers in Neuroscience, 17, p.159.
10. Samuel, O.W., Asogbon, M.G., Geng, Y., Pirbhulal, S., Mzurikwao, D., Chen, S., Fang, P. and Li, G., 2018, October. Determining the optimal window parameters for accurate and reliable decoding of multiple classes of upper limb motor imagery tasks. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS)(pp. 422-425). IEEE.
11. Mzurikwao, D., 2020. Application of deep neural network in healthcare data. University of Kent (United Kingdom).

Contacts

Email: Deogratias.Mzurikwao@muhas.ac.tz

dmzurikwao@gmail.com