Machine learning could help predict defects, fraud and cancer

When Dr. Shen-Shyang Ho looks at a graph, he sees more than abstract data points. In the dynamic graphs he studies, the computer science researcher sees complex networks that change over time. An associate professor in the Department of Computer Science in Rowan University's College of Science & Mathematics, Ho has studied and developed machine-learning technologies for detecting anomalies in various application domains for nearly 20 years. "Anomalies are deviations from the normal," explained Ho, who also coordinates Rowan's master's degree program in computer science. Many anomalies are undesirable, he added, such as financial fraud, suspicious behavior, manufacturing defects and abnormal findings on medical tests.