Trustworthy Scientific Machine Learning

Trustworthy machine learning for geo-distributed scientific data analytics.

Abstract: This project aims to develop a trustworthy optimization toolbox for geo-distributed scientific data analytics, addressing gaps in AI/ML practices where models trained on historical or regional data struggle with complex and evolving dynamics of phenomena like extreme weather events and climate change. The project pioneers optimization methods to enhance prediction robustness, explanation reliability, and scalable privacy protections, crucial for rare or unseen scenarios in safety-critical applications. It pursues three aims: bridging data topology and robust optimization, revolutionizing explainable machine learning for scientific discovery, and ensuring trustworthy collaborative learning. The project integrates these advancements into education, promoting diversity and inclusion in STEM through interdisciplinary outreach and curricula.