Education
PAI links short-format convening, Harvard coursework, and research-facing education for students and practitioners working across grid systems and intelligent infrastructure.
Current short course
Planning is underway for the next Power Systems and Artificial Intelligence short course. Dates and format will be posted here once the program is finalized.
Short course
The next short course is in development for 2026. It will continue PAI’s approach of teaching power systems and AI as one shared technical domain shaped by infrastructure, reliability, data, and operations.
Earlier programs remain viewable so visitors can understand the structure and scope of the initiative’s professional education work.
May 21, 2025
Wednesday, May 21, 2025
The inaugural short course brought together professionals and researchers for a one-day introduction to grid operations, AI methods, and energy-efficient AI infrastructure.
View 2025 short courseHarvard courses
Professor Le Xie
ES 145
This course begins with a solid foundation in classical power system engineering, covering the basic operation and control of electric grids. It then explores the grid’s transformation into a data-rich, power electronically interfaced system. It introduces modern AI-driven approaches for forecasting, renewable integration, and smart grid operations. Through hands-on learning with simulation software, realistic data sets, and case studies, students will bridge engineering fundamentals with data science applications. The course equips students with tools to understand, model, and design sustainable and intelligent power systems in the age of AI.
ES 215
This course introduces graduate students to operational issues in sustainable electric energy systems. The first part covers basic electrical engineering, optimization, and economic concepts. The second part examines the “modular” view of energy processing components (e.g., generators, transmission network, demands). The third part explores physical and market operations in the evolving electricity industry. Computer-based demos and homework will help students understand key concepts relevant to the power industry.
Professor Minlan Yu
CS 1450
This course studies computer network topics including Layer 2/Layer 3 topology, routing, transport protocols, traffic engineering, network functions, programmable switches, and software-defined networking. Modern networks have grown to large scale (connecting millions of servers) and high speed (terabits per second) to meet the needs of cloud applications in business and society. Thus, in addition to learning the conventional concepts in networking, we will also discuss how to adapt these concepts to large-scale networks. These discussions will hopefully help deepen our understanding of networking technologies.
CS 2430
This is a graduate-level course on computer networks, offering an in-depth exploration of selected advanced topics in networked systems. We will discuss the latest developments across the entire networking stack, the interactions between networks and high-level applications, and their connections with other system components such as computing and storage.
Learning model
Power systems foundations
Students build a grounded understanding of electric-energy systems, not only AI abstractions layered on top of infrastructure.
AI methods
Education at PAI treats AI as a full-stack systems question, linking modeling, control, forecasting, networking, and compute.
Community
The initiative ties classroom work to seminars, workshops, and collaborative research so that education stays connected to practice.