Efficiently modeling high traffic areas will maintain the level of safety in autonomous vehicles and other drivers while reducing the computational cost.
Yi (Max) Ren is an assistant professor of aerospace and mechanical engineering in the School for Engineering of Matter, Transport and Energy at Arizona State University. Ren’s current research interests include optimization, product/configuration design, human-computer interaction and machine learning.
Total projects: 8
Creating a time-variant neural network that can model particle dynamics will demonstrate machine learning can learn unknown physics concepts.
Machine Learning and Dimensionality Reduction to Accelerate DFT Simulations of Material Properties and Enable Computational Material Design
Using machine learning for material property simulations will accelerate the process and decrease computational expense.
Studying the ability for an autonomous vehicle to determine another vehicle's intent or loss of function will promote safer executions of traffic scenarios.
By attempting to exploit learning systems in autonomous vehicles, we can better characterize the safety of the next disruptive technology.
Validating the reliability of autonomous vehicle control models will ensure autonomous car machine learning systems can’t be poisoned.