Google Scholar: https://scholar.google.com/citations?user=Rbk1ttIAAAAJ&hl=en
Her areas of expertise include machine learning, deep learning, environmental analysis, remote sensing, and natural disaster prediction, with published research on flood severity classification in Bangladesh. She possesses extensive proficiency in programming languages, frameworks, and data science tools, including Python, PyTorch, Scikit-Learn, and Gradient Boosting Machines (GBMs).
In addition to her academic pursuits, she has gained industry experience as a Software Engineer Intern at Selise Digital Platforms, where she contributed to the development of e-commerce applications using the MEAN stack. She is deeply committed to interdisciplinary research, student mentorship, and academic excellence, actively engaging in thesis supervision and academic counseling.
Beyond her professional responsibilities, she has held leadership positions in student organizations, participated in national-level technology competitions, and remains dedicated to continuous learning and professional development through workshops, conferences, and research initiatives.
Nishat, F.Z., Nahar, N., Joti, F.I., Islam, S., Adri, N. and Ahmed, M.U., 2025. Flood severity classification in Bangladesh: a comprehensive analysis of historical weather and water level data using machine learning approaches. Natural Hazards, [online] Available at: https://doi.org/10.1007/s11069-025-07202-6 [Accessed 16 March 2025].