FURI | Fall 2018
Classification of Multi-Agent Interaction in Biological Systems Using Machine Learning
Interactions among biological agents are of scientific value. These interactions may be used by agents to form strategies, cooperate with one another, or establish a hierarchy. The ability to classify and predict these interactions enables researches to derive models from them. These models can then be applied to a broader context, such as cyber-security, to better understand malicious and cooperative behavior among botnets. The purposes of interactions vary, the classification process does not. The researchers have proposed a general methodology for classifying video footage of interactions within biological colonies. The process is a step toward prediction of live systems.
Hometown: Phoenix, Arizona
Graduation date: Fall 2018