The NIEHS Superfund Research Program (SRP) is hosting a Risk e-Learning webinar series focused on using artificial intelligence (AI) and machine learning to advance environmental health research. The series will feature SRP-funded researchers, collaborators, and other subject-matter experts who aim to better understand and address environmental health issues by applying AI and machine learning approaches to complex issues.

Recent advances in AI and machine learning methods show promise to improve the accuracy and efficiency of environmental health research. Over the course of three sessions, presenters will discuss how they use AI and machine learning approaches to improve chemical analysis, characterize chemical risk, understand microbial ecosystems, develop technologies for contaminant removal, and more.

In the second session ML & AI Applications to Environmental Engineering Contaminants & Bioremediation, invited presenters will discuss how they apply machine learning and artificial intelligence to environmental engineering applications including contaminants and bioremediation using biosensors, microbiome compositions, and screening tools.

To learn about and register for the other sessions in this webinar series, please see the SRP website.

Kei-Hoi Cheung, Ph.D., has an extensive history in data science, and has leveraged that expertise to lead natural language processing (NLP) projects in annotating, extracting, and retrieving environmental exposure data. He will present on the use of these NLP methods combined with ontologies in the in the context of scientific literature on emerging water contaminants.

Mohammad Soheilypour, Ph.D., will discuss the application of a suite of computational methods to identify and predict microbial metabolism of various chemical compounds, with a focus on gut and environmental microbiomes. Specifically, he will cover the potential application of machine learning models in this context and their integration with other computational methods to enhance both accuracy and utility.

Paul Westerhoff, Ph.D., will highlight the work of his research team utilizing and comparing two advanced multiple data imputation techniques, AMELIA and MICE algorithms, to fill gaps in sparse groundwater quality datasets to support State agencies in prioritizing future sampling activities. Historic water quality databases are often sparse due to financial budgets for collection and analysis, posing challenges in evaluating exposure or water treatment effectiveness — and this project aims to account for those by accurately assessing and managing risks associated with inorganic pollutants using this technology.

Speakers:Kei-Hoi Cheung, Ph.D., Yale University School of MedicineMohammad Soheilypour, Ph.D., Nexilico Inc.Paul Westerhoff, Ph.D., Arizona State UniversityModerator: Rodrigo Rimando, U.S. Department of Energy To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/SRP-ML-AI2_112024/