About Me
I am a Climate Scientist and Visiting Research Fellow at Harvard University’s Data Science Initiative, HDSI in Cambridge, MA. I completed a postdoctoral fellowship at Princeton University, advised by Prof Michael Oppenheimer. I obtained my DPhil (PhD) in Climate Science with a focus on climate attribution and climate econometrics from the University of Oxford, and obtained a MS in Sustainability Management from Columbia University.
My research approach is strongly interdisciplinary and collaborative, integrating numerical models and data science methods from probabilistic event attribution and econometrics to answer research questions related to estimating socio-economic impacts of extreme weather and climate change. The main questions that underlie my ongoing research activities include:
1) Whether and to what extent does anthropogenic climate change impact the frequency and intensity of extreme weather events and resulting human activities and economic outcomes?
2) What are the human responses and health-related sensitivities to climate change?
3) What disaster risk reduction mechanisms prepare populations and infrastructure before natural hazards turn into disasters?
In my spare time, I can be found hiking with my 2-year-old, learning Yiddish (or trying to?) and embracing ‘ready, steady, cook’ sessions, creating sustainable new recipes from leftovers.
Latest Research
Socio-economic dimensions of climate change, published in Nature Communications
We underscore the economic dimensions of involuntary immobility—a critical but often ignored consequence of climate change, where individuals and communities are unable to migrate away from high-risk areas due to financial, structural, and systemic barriers. While climate change intensifies natural hazards, economic constraints—such as poverty, lack of access to capital, and limited livelihood alternatives—prevent vulnerable populations from relocating, even when staying poses severe risks. In our study, we highlight how economic disparities exacerbate climate vulnerability: those with fewer resources face greater exposure to climate impacts and fewer options to escape them. Addressing this challenge requires policies that not only reduce economic barriers to migration but also invest in local adaptation, ensuring that climate action is equitable and supports both mobility and resilience for those who cannot or choose not to move. Without targeted economic interventions, climate change will deepen inequality and leave the most vulnerable populations further behind.
Bayesian modeling assessing short-term conflict-displacement dynamics, published in Annals of Applied Statistics
Together with Gregor Zens, I investigate methodological challenges when modeling imperfect data collected in conflict zones. Employing a Bayesian panel regression, we aim to understand why and under what conditions higher temperatures and meteorological drought lead to conflict-related migration. The results suggest a rapid and non-linear migration response post-conflict, with significant heterogeneity in effects dependent on the nature of conflict events. Our model outperforms standard benchmarks in a forecasting exercise, underscoring its potential for informing decision-makers in crisis scenarios.
Advancing flood displacement modelling, published in Earth’s Future
The 2022 summer flood in Pakistan displaced about 7 million people in the Sindh province alone. Up to one-third of the country’s area was flooded. Using the open-source impact assessment platform CLIMADA, we study the extent to which flood-related hazards can be used to quantify displacement outcomes in a data-limited region. Using flood depths, exposed population, and impact functions, we link flood vulnerability to displaced people. This allows us to estimate internal displacement resulting from the flood event and assess how displacement varies across different areas. We find that a flood depth threshold of 0.67m (CI 0.35 - 1.10) provides a best fit to all data from Sindh province. We find a negative correlation between displacement and the degree of urbanisation. By testing our model’s performance in explaining differences in displacement estimates across Pakistan, we demonstrate the limitations of existing impact assessment frameworks.
AI for Early Warning Systems and reducing climate change impacts, published in iScience
Artificial intelligence (AI) is increasingly being used to analyze natural hazards and their impacts, which is crucial for improving Early Warning Systems (EWS). These systems help protect people and communities by providing timely, actionable alerts about risks such as floods, storms, and earthquakes. However, AI’s potential in EWS remains underutilized, especially in risk assessment, forecasting, communication, and preparedness. This study reviews how AI is currently used in EWS, identifies challenges and opportunities, and emphasizes the need for responsible AI frameworks. The goal is to ensure that AI-powered warning systems are inclusive, accessible, and focused on people’s needs, supporting global efforts to make early warnings available to everyone by 2027.