Ever wonder how engineers tackle complex real-world challenges?
Dive into the rapidly evolving world of modern engineering in this one-week, project-based course. Experience hands-on challenges in mechanics, autonomous systems, and thermal-fluid science while coding in Python and MATLAB, with generative AI tools as your virtual assistant. No advanced math required — just bring your curiosity and creativity!
Work on real-world engineering projects that apply neural networks, decision trees, and other AI methods, all while building essential skills for a future STEM career. Perfect for students eager to experience engineering through a fresh, accessible approach that integrates artificial intelligence with human ingenuity to enhance problem solving.
By the end of the course, students will:
- Understand the basics of coding with MATLAB or Python for engineering applications.
- Use generative AI tools to assist in problem formulation, coding, and debugging.
- Solve fundamental problems across mechanical, civil, electrical, and materials engineering disciplines.
- Develop computational models to visualize and analyze complex engineering systems.
- Gain confidence in approaching complex problems using computational tools and basic mathematical modeling.
Engineering Problem-Solving With AISESSION DATES
APPLICATIONS OPEN IN JANUARY |
Experiential Learning
This course combines short lectures, tutorials, and immersive hands-on activities using AI-assisted problem formulation, coding, and analysis on the following topics:
- Engineering computing and AI tools
- Mechanics: motion and forces
- Thermodynamics: energy and efficiency
- Fluid dynamics: flow and pressure
- Capstone project: solve a real-world engineering problem
Instructor
Carlos Colosqui, PhD
Associate Professor and Undergraduate Program Director, Department of Mechanical Engineering
Affiliated Faculty, Applied Mathematics & Statistics Department
Prof. Carlos Colosqui is the undergraduate program director in mechanical engineering and affiliated faculty in The Applied Mathematics & Statistics Department. His research work combines theoretical and computational modeling with experimental studies of transport processes in engineering applications ranging from advanced manufacturing of nanomaterials to electrochemical energy storage systems. He is the recipient of multiple awards from The National Science Foundation (NSF), The Department of Energy (DOE), Office of Naval Research (ONR), and The New York State Research and Development Authority (NYSERDA). He is a senior principal investigator in the DOE Energy Research Frontier Center for Mesoscale Transport Properties (EFRC m2M), which researches batteries and advanced energy storage systems.
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