Interdisciplinary Scientific AI Supercomputing Hub (ISAS)

The Interdisciplinary Scientific AI Supercomputing Hub (ISAS) is a cutting-edge research and teaching facility at the University of Illinois Springfield (UIS). It provides expertise in statistical methodology, modeling and computational design, data analytics, and data visualization, supporting both students and faculty in their research and academic endeavors.

Housed within the College of Health, Science, and Technology, ISAS serves as a hub for interdisciplinary collaboration, providing expert consulting services to students, faculty, researchers, and external organizations. In today's data-driven world, ISAS aims to bridge the gap between data collection and effective analysis, empowering stakeholders to make informed decisions grounded in rigorous statistical and computational principles.

Research Interests

Big Data Analytics: Big data analysis involves processing and extracting insights from large datasets gathered from experiments, observational studies, and other sources. It includes data management, visualization, statistical inference, and iterative updates as new data emerges.

AI-powered Modeling: AI-powered modeling uses artificial intelligence, like machine learning and deep learning, to enhance or replace traditional mathematical models. It learns patterns from data, optimizes simulations, and improves predictions, making it valuable for complex scientific computing. At ISAS, we use AI to combine traditional methods (e.g., differential equations) with data-driven techniques, enabling more efficient, adaptive, and scalable modeling across disciplines like physics, engineering, and biology.

Scientific Machine Learning (SciML): SciML blends machine learning with traditional scientific computing to solve complex mathematical and physical problems. It integrates AI techniques with numerical methods, improving accuracy, efficiency, and adaptability in modeling and simulation.

AI-Enhanced Education: At ISAS, we explore AI-driven technologies to improve student learning. Our focus includes AI-powered tutoring systems for personalized feedback, VR/AR integration for interactive simulations, and data-driven insights to optimize teaching strategies. These innovations make education more engaging, adaptive, and effective.