Climate Informatics

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Climate informatics is an emerging field that sits at the intersection of climate science and data science. It harnesses the power of statistical and machine learning methods to interpret complex climate data. My research interest lies in the innovative application of climate informatics to address critical societal challenges, specifically in public health, energy, and socio-economic climate inequality.

In the realm of public health, I explore how climate variables influence health outcomes. By analyzing patterns and trends in climate data, I aim to predict and mitigate health risks associated with climatic changes, such as heatwaves and air quality This approach not only helps in proactive public health planning but also in understanding the broader impact of climate change on human health.

Energy is another crucial domain where climate informatics plays a transformative role. My research includes developing predictive models that forecast energy demand and supply fluctuations driven by climatic conditions. These models are instrumental for optimizing energy generation and distribution, especially in the context of renewable energy sources like solar and wind, which are highly dependent on weather conditions.

Lastly, addressing climate inequality from a socio-economic perspective is a significant aspect of my work. Climate change does not affect all communities equally; vulnerable populations often bear the brunt of its adverse effects. By integrating climate data with socio-economic indicators, I aim to identify and address these disparities. This includes studying the impact of climate change on various socio-economic groups and developing strategies to ensure equitable access to climate change mitigation and adaptation resources.

Relevant Peer-Reviewed Papers