Brian Kulis
NRT: ENERGIZE ? Interdisciplinary graduate program integrating data science in energy materials research
The global population has skyrocketed from approximately one billion in 1800 to over eight billion in 2024, driving increased demand for vital and interrelated resources like energy, food, and water. Sustainable approaches are required to meet the escalating energy demand, including resource conservation, energy storage enhancements, and global energy resource management. Addressing the research challenges […]
Automated Analytics for Improving Efficiency, Safety, and Security of HPC Systems
Performance variations are becoming more prominent with new generations of large-scale High Performance Computing (HPC) systems. Understanding these variations and developing resilience to anomalous performance behavior are critical challenges for reaching extreme-scale computing. To help address these emerging performance variation challenges, there is increasing interest in designing data analytics methods to make sense out of the […]
AI-based Scalable Analytics for Improving Performance, Resilience, and Security of HPC Systems
Next generation large-scale High Performance Computing (HPC) systems face important cost and scalability challenges due to anomalous system and application behavior resulting in wasted compute cycles and the ever-growing difficulty of system management. There is an increasing interest in the HPC community in using AI-based frameworks to tackle analytics and management problems in HPC so […]
CAREER: Rich and Scalable Optimization for Modern Bayesian Nonparametric Learning
Large-scale data analysis has become an indispensable tool throughout academia and industry. When the amount of data is very large, one often faces a tradeoff between the richness, flexibility, and potential predictive power of the models, and the computational requirements. While recent advances in statistics and machine learning provide us with a rich set of […]