Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to
assesses the mechanisms through which climate change impacts mosquito-borne diseases …

Global climate models are central tools for understanding past and future climate change.
The assessment of model skill, in turn, can benefit from modern data science approaches …

The parameterization of moist convection contributes to uncertainty in climate modeling and
numerical weather prediction. Machine learning (ML) can be used to learn new …

A number of studies have demonstrated the importance of ozone in climate change
simulations, for example concerning global warming projections and atmospheric dynamics …

Climate field reconstructions (CFRs) combine modern observational data with paleoclimatic
proxies to estimate climate variables over spatiotemporal grids during time periods when …

Downscaling is a common task in climate science and meteorology in which the goal is to
use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling …

As applications of machine learning proliferate, innovative algorithms inspired by specific
real-world challenges have become increasingly important. Such work offers the potential …

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from
sequences of probability distributions, even where the random variables are high …

In this position paper, we argue that application-driven research has been systemically
under-valued in the machine learning community. As applications of machine learning …