Education

This course covered the basics of data science methods for the design and operation of energy systems. It was geared towards students aiming for a technical career in cleantech, or a PhD in energy sciences and engineering. The course content included mathematical modeling & analysis, state estimation, optimization, machine learning, and optimal control. Homework assignments were designed around case studies, such as lithium-ion batteries, geothermal drilling systems, renewable power systems, smart buildings, and electrified transportation.

The course provided an overview of the recent and potential future evolution of electric power systems, with a focus on new and emerging technologies for power conversion and delivery, primarily at the distribution level. Topics covered included power electronics applications, solar and wind generation, distribution system design and operation, electric energy storage, information management and communications, demand response, and microgrids.

This course covered topics concerning the intricate relationship between optimization and statistical learning. The initial segment of the course explored statistical modeling procedures, which could be defined as the minimizer of an appropriate optimization problem. The subsequent part of the course focused on the formulation and numerical implementation of learning-based model predictive control (LBMPC). This method, known for its robust adaptive optimization, leverages machine learning for adaptation. The concluding part of the course addressed inverse decision-making problems, where we observed an agent's decisions to infer characteristics about the agent.

The course covered the methods and tools of environmental life-cycle assessment (LCA), an international standard, with a particular emphasis on infrastructure, but also applicable to other products, processes, services, and systems. It focused on the design, management, and supply chains encompassing the entire life cycle: manufacturing, construction, transportation, operation and maintenance, and end of life. The course also covered economic life-cycle cost analysis. It provided models and tools for life-cycle environmental inventory, impact, and improvement analysis.

One year capstone project. Designed a low-cost walker and pit latrine aid utilizing locally sourced materials and manufacturing methods for landmine survivors in Uganda. Traveled to Uganda with a team of five students and one professor. Spent two weeks teaching vocational students how to build the walkers and distributing walkers to landmine survivors.

This course covered the thermodynamics of traditional fossil fuels and biofuel combustion. It included an analysis of solar, wind, wave, and tidal power systems. An introduction to fuel cells and advanced battery technology was also provided. Furthermore, the course involved a discussion on the current technological limitations of each topic listed above.

The course offered an interdisciplinary overview of the engineering roles and opportunities aimed at enhancing the sustainability of engineering products, processes, and systems. Topics covered included carbon footprint, life cycle assessment, design for sustainability, wastes and recycling, as well as energy and water.

The course involved an introduction to data science and analytics using R. Topics included linear programming, linear regression, random forests, and k-means clustering.