Background
I have an interdisciplinary background spanning general medicine, development studies, public health, and environmental science, with experience across academia, non-governmental organizations, and international agencies. At Kathmandu University, in collaboration with the University of Bristol, I conducted research on climate-resilient Water, Sanitation and Hygiene (WASH). I later collaborated with an NGO and the University of Sheffield on public health equity research. Most recently, at WHO Nepal, I supported the piloting of the WHO-TDR Early Warning, Alert and Response System (EWARS), working closely with national health, meteorological, and drinking water agencies.
These experiences have shaped my research interest in climate health—understanding how climate variability and change influence public health risks, and how integrating climate, health, and environmental data can strengthen decision-making, support climate adaptation, and build resilient health systems.
Interests
My research focuses on the intersection of climate, health, and digital systems, with particular interest in designing Climate Health Information Systems (CHIS) that are effective, adaptive, and user-centred. I am interested in how these systems can be translated from technical innovations into practical tools that support decision-making in real-world public health settings.
More broadly, I seek to advance the design, implementation, and integration of climate-informed Early Warning, Alert and Response Systems (EWARS) within health systems. My work also explores how CHIS perform in practice, generating evidence to inform capacity strengthening, institutional learning, cross-country knowledge exchange, and sustainable climate health governance.
My Project
My postdoctoral project, PASTA-ML (Participatory Socio-Technical Framework for Actionable Machine Learning-Driven Climate Health Prediction, Alerts, and Decision Support), is part of the DStrain programme and is embedded within the Health Information Systems Programme (HISP) Centre's climate health initiatives.
The project investigates how machine learning-driven climate-health models can be made more usable, trustworthy, and relevant for implementers and end users. It adopts a participatory socio-technical perspective to understand how complex climate-health information systems can be designed, simplified, and embedded within routine public health practice. Ultimately, the project aims to develop a practical framework to guide the design, implementation, and adaptation of actionable CHIS across diverse contexts.
Collaboration
I welcome collaborations that combine socio-technical perspectives with data-driven approaches to strengthen climate-health information systems and digital public health. My research interests include climate-health data integration, participatory system design, implementation and evaluation of CHIS, legitimacy and governance of digital health systems, EWARS design and performance, and the development of resilient, adaptive health systems capable of responding to climate-sensitive health risks.
Through interdisciplinary and collaborative research, I aim to bridge climate science, digital innovation, and public health practice to support evidence-informed, equitable, and climate-resilient health systems.