NEO 2025, Tijuana, BC, Mexico, September 23 - 26, 2025

Jian-Qiao Sun

What Machine Learning Can Do for Engineering

Abstract:

This talk presents discussions on what we can do with machine learning for engineering research. Specifically, we shall discuss the following questions without giving the audience affirmative answers. It is hoped that this talk will motivate more people to learn various methods from the machine learning and artificial intelligence community and apply them to investigate new solutions of engineering research problems.

• 1. What does machine learning really do?
• 2. Why do we call the method learning, not solving?
• 3. How do we define the intelligence?
• 4. How do we make the ML solution intelligent?
• 5. Why is it important to study algorithms in the AI age?
• 6. Why digital twin is a dream of engineers?
• 7. What have we done in dynamics and control for engineering applications?

Some examples of neural networks based control of robots, nonlinear optimal controls and other topics will be presented.

Ryan J. Urbanowicz

Dr. Ryan J. Urbanowicz is a Research Assistant Professor in the Department of Computational Biomedicine at Cedars-Sinai Medical Center in Los Angeles. His research interests lie at the intersection of genetics, genomics, biostatistics, epidemiology, machine learning, and artificial intelligence. He has adopted a quantitative biomedical research strategy that embraces, rather than ignores, the complexity of the relationship between predictive factors and disease endpoints. Dr. Urbanowicz earned his Ph.D. in Genetics from Dartmouth College's Geisel School of Medicine, a Master of Engineering in Biological Engineering and a Bachelor of Science in Biological and Environmental Engineering, both from Cornell University.