Courses

This course focuses on the application of AI to energy systems. Students will explore how AI techniques can optimize the performance of energy and power systems, with a particular focus on sustainable energy systems and renewable energy transition. Key topics include the optimization of energy generation, distribution, storage, and consumption. Specific case studies will cover topics such as optimizing solar and wind energy integration into the grid, improving battery storage management for renewable energy, and enhancing energy efficiency in smart grids. The course will also highlight AI applications in balancing supply and demand for renewable energy systems. This course is part of the AI4S NRT curriculum.

This course focuses on the application of artificial intelligence (AI) in the digital transformation of agriculture. Students will explore how AI techniques are applied to optimize and automate agricultural systems, improve productivity, and enhance sustainability. The course covers a broad range of topics, including AI-driven crop management, precision farming, livestock monitoring, and data analytics for sustainable agriculture. Case studies on AI applications in plant and animal production systems, as well as food supply chains, will provide practical insights into the future of farming. Students will engage in discussions on the ethical, social, and economic implications of AI in agriculture, while hands-on projects will offer experience in applying AI tools to real-world agricultural challenges. This course is part of the AI4S NRT curriculum.

This course focuses on the application of artificial intelligence (AI) in materials science. Students will explore how AI and machine learning techniques can accelerate the design, discovery, and optimization of materials for energy storage, conversion, and sustainability. Key topics include using AI to predict material properties, enhance materials synthesis, and model complex material behaviors. The course emphasizes practical applications of deep learning models in materials science, featuring hands-on projects and case studies from recent research. Students will also discuss the challenges and opportunities of applying AI to advance innovations in materials science. This course is part of the AI4S NRT curriculum.

This is a studio-style course that emphasizes collaborative learning and innovation in sustainability. Students will explore foundational and cutting-edge literature, research, and potential future directions in AI for Sustainability. The course will cover a range of topics related to the use of AI and machine learning in sustainability science and engineering, including energy systems decarbonization, sustainable agriculture, climate modeling, resource optimization, and biodiversity conservation. Students will gain hands-on experience with AI/ML methodologies, tools, and software and engage in discussions on the latest advancements and applications of AI in addressing global sustainability challenges. This course is part of the AI4S NRT curriculum.

This course covers systems optimization theory, algorithms and applications. Topics include the fundamentals of linear programming, integer programming, nonlinear programming, mixed-integer nonlinear programming, deterministic global optimization, stochastic programming, robust optimization, data-driven optimization, and optimization methods for big data analytics. The focus is on real-world application problems in manufacturing, aerospace, control, management, infrastructure, energy systems, sustainability, and data science. We are also developing a Cornell University Computational Optimization Open Textbook.

This course is part of the Computational and Data Science Certificate program.

This course covers the basic concepts, models and algorithms of Bayesian learning, classification, regression, dimension reduction, clustering, density estimation, artificial neural networks, deep learning, and reinforcement learning. Application and methodology topics include process monitoring, fault diagnosis, preventive maintenance, root cause analysis, soft sensing, quality control, machine learning for process optimization, data-driven decision making under uncertainty, missing data imputation, data de-noising, and anomaly/outlier detection.

This course is part of the Computational and Data Science Certificate program.

This course introduces the current status of quantum computing and its applications for artificial intelligence. Topics include Qubit states, superposition, entanglement, interference, hardware platforms, quantum circuits, quantum speedup, Ising model, quantum algorithms, adiabatic quantum computing and quantum annealing, noisy intermediate scale quantum (NISQ) computing, quantum optimization, quantum machine learning, and quantum neural network. Applications of quantum computing to chemistry, materials, finance, transportation, and energy will be discussed.

Discussions with first-year students on engineering curriculum and student programs, what engineers do, the character of engineering careers, active research areas in the college and in engineering in general, and study and examination skills useful for engineering students.

Discussions with first-year students on engineering curriculum and student programs, what engineers do, the character of engineering careers, active research areas in the college and in engineering in general, and study and examination skills useful for engineering students.

Students prepare a full-scale feasibility study of a chemical process including product supply and demand forecasts, development of mass and energy balances and a process flow sheet sufficient for estimating the capital and operating costs of the process facilities. Students also define all off plot support facilities and estimate the capital and operating costs of those facilities as well. This information is used to develop an economic analysis of the facilities and to provide an ultimate recommendation as to the viability of the project.

Design of chemical and process plants applying the principles of unit operations, thermodynamics, reaction kinetics, separation and economic analysis. Team projects aim to develop ability to communicate effectively (written and oral). Basic concepts on process safety and process intensification are introduced. Recent design problems focus on the manufacturing of chemicals and fuels from renewables. Together with the students and the TAs, we are developing the Northwestern University Chemical Process Design Open Text Book along with this course.

Introduces modern optimization theory and algorithms, including linear programming, integer programming, nonlinear programming, and mixed-integer nonlinear programming. Presents optimization applications in chemical and biological engineering and develops students’ ability to formulate large-scale optimization models for real-world applications. Special topics on decomposition algorithms and optimization under uncertainty are also offered at the end of the course. Team projects focus on the application of optimization techniques to solve research problems in process engineering.

This course covers the fundamentals of heat transfer, with particular emphasis on the mechanisms of conduction and convection. In addition to basic principles and analysis, applications to the design of heat transfer equipment in the process industries are discussed.