Date：2025/9/2 11:00-12:10
Location：R105, CSIE
Speakers：Chuanyi Ji
Host：林軒田教授
Abstract:
Energy demand has been increasing dramatically driven by AI and its applications. Clean energy becomes a clear choice for supporting the industrial growth. Significant challenges arise. The first is how to provide reliable energy delivery in a changing climate, where severe weather events are also on the rise, disrupting energy services. The second is how to access the potential of clean energy at a large scale for energy resilience.
In this talk, we first discuss whether machine learning algorithms are feasible in learning from
energy data. We then present real world data and analytics, showing how learning algorithms
offer both insightful knowledge and software tools. Finally, we pose open issues for future
study.
Chuanyi Ji is on the faculty at the school of ECE, Gatech. Her research is on large-scale data analytics and machine learning for networks. Her recent work includes resilient energy networks and impact on people as well as clean energy for cities. The methodology development includes spatial temporal models for learning from micro data at scale.
In this talk, we first discuss whether machine learning algorithms are feasible in learning from
energy data. We then present real world data and analytics, showing how learning algorithms
offer both insightful knowledge and software tools. Finally, we pose open issues for future
study.
Chuanyi Ji is on the faculty at the school of ECE, Gatech. Her research is on large-scale data analytics and machine learning for networks. Her recent work includes resilient energy networks and impact on people as well as clean energy for cities. The methodology development includes spatial temporal models for learning from micro data at scale.
Biography:
School of Electrical and Computer Engineering
Georgia Institute of Technology
Atlanta, Georgia