The Association for Computing Machinery awarded computer science and engineering graduate student third place in the Student Research Competition for his work on code documentation.
“The goal of this project is to develop SmartScript, a novel static code analysis that automatically detects type and semantic inconsistency errors in dynamic scripts, such as shell scripts, Python, LUA, Javascript, etc,” Huang said.
A team of researchers from Texas A&M University, Yale University and the American College of Cardiology investigated how machine learning techniques can be applied in health care to predict the probability of a patient bleeding during or after a coronary procedure.
