Description:
Recent years have seen a surge in the development and application of machine learning (ML) and artificial intelligence (AI) technologies, revolutionizing how we approach data-driven discoveries across diverse scientific domains. Environmental science, health, and engineering are areas of increasing complexity, where issues such as pollution, climate change, and public health require innovative solutions. AI and ML methods offer a powerful means of addressing these challenges, enabling the prediction of environmental impacts, toxicities, and risks, and supporting better-informed policy and regulatory decisions. We shall invite the submission of abstracts that apply machine learning and AI methodologies to solve pressing problems in environmental science, health, and engineering. Topics of interest include, but are not limited to: Source Attribution, Chemical Toxicity Prediction, Screening of Unknown Pollutants, Human Exposure Assessment, Molecular Mechanisms of Exposure and Disease, Generative Models for Molecular Design and Optimization, Benchmarking Studies.
Organizer:
Joseph Wasswa