-
. The core research goals are to: Develop a probabilistic machine learning tool that can determine the optimal grinding parameters for different scenarios based on required material removal depth and rail
-
Search-Based Testing (SBT) and Diversity-Based Testing (DBT) to look for conditions that cause failure, such as excessive force or jamming. Bridging the Gap: Simulation vs. Real World: Compare how well
-
markers. Develop machine learning models capable of predicting Category 1 emergencies based on real-time audio features extracted from calls. Work iteratively with YAS researchers to test and refine
-
at higher risk offered PSA blood tests which are not definitive. Our research aims to develop an image-based approach to screening, combining PSA testing with MRI to better identify aggressive cancers