Marzia has a multidisciplinary background of Artificial Intelligence, Electrical and Electronic Engineering and Materials Science. She received her PhD from the Anglia Ruskin University, UK in 2019 with the scholarship from the Erasmus Mundus Action 2 FUSION project. The focus of her doctoral research was on intelligent systems for pathological tests using computer vision and machine learning on point-of-care platforms.
Marzia has 9 years+ research and field-level experience to work in the digital healthcare sector, including in the Climate Change and Health Promotion Unit, Ministry of Health and Family Welfare, Bangladesh. Prior joining CHI Lab, she served as a research fellow in the Medical Technology Research Centre (MTRC), Anglia Ruskin University in the areas of computer vision, machine learning and intelligent systems for unmet and urgent health and social care needs. Earlier in the same university, she has worked in several projects such as the TB project in collaboration with Universiti Putra Malaysia, supported by the Newton Fund; on drug usage in the construction industry in collaboration with University of Colorado, USA.
Marzia joins CHI Lab as an EPSRC supported postdoctoral researcher. Her research focuses on machine learning with electronic health records.
Marzia is working on a number of projects, with special attention towards cancer risk prediction using electronic health records.
Hoque Tania, Marzia, Kaiser, M Shamim, Abu-Hassan, Kamal, Hossain, M. A., 2020. Pathological Test Type and Chemical Detection Using Deep Neural Networks: A Case Study Using ELISA and LFA Assays. Journal of Enterprise Information Management. DOI: 10.1108/JEIM-01-2020-0038 (accepted)
Hoque Tania, M., Lwin, K. T., Shabut, A. M., Najlah, M., Chin, J., 2020. Intelligent Image-Based Colourimetric Tests Using Machine Learning Framework for Lateral Flow Assays. Expert Systems with Applications, 139, 112843.
Shabut, A. M., Hoque Tania, M., Lwin, K. T., Evans, B. A. Yusof, N. A., Abu-Hassan, K. J. & Hossain, M. A., 2018. An Intelligent Mobile-Enabled Expert System for Tuberculosis Disease Diagnosis in Real Time. Expert Systems with Applications, 114, pp. 65-77.
Hoque Tania, M., Lwin, K. T., & Hossain, M. A., 2018. Advances in Automated Tongue Diagnosis Techniques. Integrative Medicine Research, 8 (1), pp. 42-56. Sherratt, F., Welfare, K., Hallowell, M., Hoque Tania, M., 2018. Legalized Recreational Marijuana: Safety, ethical, and legal perceptions of the workforce. Journal of Construction Engineering and Management, 144 (6).
Full List can be found at Google Scholar.