Contact information
LinkedIn: https://www.linkedin.com/in/irfhana/
Irfhana Zakir Hussain is a Vanier Scholar and an interdisciplinary researcher working at the intersection of Artificial Intelligence, Public Health, and Climate Change Adaptation. With a background in Computer Science and Engineering, she brings strong expertise in big data ecosystem design, machine learning and deep learning, data analytics, and IoT-enabled systems to address complex public health challenges.
Irfhana's research centers on co‑creating an AI-driven decision support system for climate change adaptation in public health. Her work focuses on developing a hyperlocal AI ecosystem that maps climate–health risks, with an initial emphasis on extreme heat events. This system is designed to enable real-time surveillance, risk prediction, and actionable decision-making to support public health practitioners and communities.
Irfhana has contributed to a range of interdisciplinary research projects, including extreme heat event monitoring and prediction, air quality monitoring and forecasting, and health misinformation detection. Driven by a strong commitment to equity-informed, community-centered innovation, she is passionate about leveraging AI to strengthen public health resilience and improve health outcomes in the face of a changing climate.
Education
Ph.D. Public Health Sciences — University of Waterloo — 2023-Present
B.Tech Computer Science and Engineering, Big Data Analytics — SRM Institute of Science and Technology, Chennai, India — 2018-2022.
Research Interests
- AI, IoT and Big Data in Health
- Climate and Health Action
- Next Generation Public Health Surveillance
- Interpretable AI
- Infodemics and Infoveillance
Selected Publications
Full List of Publications: ResearchGate
Morita, P.*, Zakir Hussain, I.*, Kaur, J.*, Lotto, M., & Butt, Z. (2023). Tweeting for Health Using Real-time Mining and Artificial Intelligence–Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter. Journal of Medical Internet Research, 25, e44356.
*Shared first authorship & contributed equally
Zakir Hussain, I., Kaur, J., Lotto, M., Butt, Z., & Morita, P. (2023). Infodemics surveillance system to detect and analyze health misinformation using big data and AI. European Journal of Public Health, 33(Supplement\_2), ckad160–163.
Lotto, M., Zakir Hussain, I., Kaur, J., Butt, Z., Cruvinel, T., & Morita, P. (2023). Exploring fluoride-free content on Twitter: A topic modeling analysis. European Journal of Public Health, 33(Supplement_2), ckad160–601.
Lotto, M., Zakir Hussain, I., Kaur, J., Butt, Z., Cruvinel, T., & Morita, P. (2023). Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study. Journal of Medical Internet Research, 25, e44586.
Lotto, M., Sa Menezes, T., Zakir Hussain, I., Tsao, S.F., Ahmad Butt, Z., P Morita, P., & Cruvinel, T. (2022). Characterization of false or misleading fluoride content on Instagram: infodemiology study. Journal of Medical Internet Research, 24(5), e37519.
S. Salim, I. Zakir Hussain, J. Kaur and P. P. Morita, "An Early Warning System for Air Pollution Surveillance: An IoT Based Big Data Framework to Monitor Risks Associated with Air Pollution," 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Dubai, United Arab Emirates, 2023, pp. 148-152, doi: 10.1109/GCAIoT61060.2023.10385123.