Total mentored projects: 11
Studying the behaviors of autonomous driving systems when encountering adversarial physical objects will help improve robustness and security.
Using neural ordinary differential equations to predict stochastic particle behavior will allow for prediction of the start and end behavior of cancer cells and tumors.
Efficiently modeling high traffic areas will maintain the level of safety in autonomous vehicles and other drivers while reducing the computational cost.
Creating a time-variant neural network that can model particle dynamics will demonstrate machine learning can learn unknown physics concepts.
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.