Division/Committee: [I&EC] Division of Industrial and Engineering Chemistry

Artificial intelligence (AI) is revolutionizing numerous fields, including Chemistry, Engineering, and Industries. This symposium delves into the exciting advancements of AI in these domains, highlighting its potential to not only optimize efficiency and productivity, but also unlock new possibilities in manufacturing and healthcare. The objective of the symposium is to (1) Showcase the latest breakthroughs and trends in AI applications for Chemistry, Engineering, and Industries. (2) Foster collaboration and knowledge exchange among researchers, engineers, and industry professionals. (3) Identify future challenges and opportunities for AI-powered advancements in these fields. This symposium aims to collect the most recent updates on data analytics and AI for healthcare and manufacturing processes. These updates can be divided into four Sessions: 1) Manufacturing, 2) Healthcare, 3) Fundamental Understanding, and 4) Other related topics. The specific areas of research can include but not limited to: AI-driven battery development and optimization Flow chemistry synthesis and reactor control AI-powered additive manufacturing (3D printing) Intelligent sensor networks and data analysis Machine vision for quality control and process automation AI-aided medical image analysis and diagnosis Personalized medicine and disease prediction Protein structure design and engineering Accelerating the discovery of small molecule drugs Physics-integrated machine learning for enhanced accuracy Model explainability and interpretability Security and governance considerations in AI deployment Theory and modeling of healthcare and manufacturing processes Fundamental understanding of driving forces and mechanism for manufacturing and healthcare Data analytics and AI in synthesis of novel materials for self-assembly, crystallization, patterning Time series analysis of sensors, energy storage and energy harvest devices Process optimization for electronics Anomaly detection, casual inference, uncertainty quantification, risk assessment for manufacturing and healthcare Data analytics and AI for new characterization, monitoring, process control, in-situ or in-operando techniques including deep learning of medical imaging Data analytics and AI for structure–property correlations in healthcare and manufacturing. Supervised and Unsupervised Machine learning and AI for healthcare and manufacturing processes Potential applications of data analytics and AI in manufacturing and healthcare

Qing Shao

University of Washington

Appearances