Novel Exploration Techniques (NETs) for Malaria Policy Interventions

The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limited tools to help them make data driven decisions. In this work we frame the process of finding an optimal malaria policy as a stochastic multi-armed bandit problem, and implement three agent based strategies to explore the policy space. We apply a Gaussian Process regression to the findings of each agent, both for comparison and to account for stochastic results from simulating the spread of malaria in a fixed population. The generated policy spaces are compared with published results to give a direct reference with human expert decisions for the same simulated population. Our novel approach provides a powerful resource for policy makers, and a platform which can be readily extended to capture future more nuanced policy spaces.Comment: Under-revie

Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows

Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduce societal harm. Cloud-based deployments help to scale up these modeling and AI workflows. Yet, for practitioners to make robust conclusions, model tuning and testing is crucial, a resource intensive process which involves the variation of model input variables. We have developed the Variational Exploration Module which facilitates the optimization and validation of modeling workflows deployed in the cloud by orchestrating workflow executions and using Bayesian and machine learning-based methods to analyze model behavior. User configurations allow the combination of diverse sampling strategies in multi-agent environments. The flexibility and robustness of the model-agnostic module is demonstrated using real-world applications.Comment: Submitted to IAAI 2024: Deployed Innovative Tools for Enabling AI Application

AI for climate impacts: applications in flood risk

Abstract In recent years there has been a surge of interest in the potential of Artificial Intelligence (AI) to address the global threat of climate change. Here, we consider climate change applications, and review the ability of AI technologies to better quantify climate change-induced hazards, impacts and risks, and address key challenges in this domain. We focus on three application areas: data-driven modeling, enabling uncertainty quantification, and leveraging geospatial big data. For these, we provide examples from flood-related applications to illustrate the advantages of AI, in comparison to alternative methods, whilst also considering its limitations. We conclude that by streamlining the process of translating weather and climate data into actionable information, facilitated by a suitable technology framework, AI can play a key role in building climate change resilience