Dr. Akbas’ broad research areas are data mining and machine learning. Specifically, with her group in Data engineering Lab (DeLab), she works on graph (network) mining, text mining, and social network analysis. One aspect of her research is developing novel effective and efficient graph processing methods. Another aspect is to conduct biomedical knowledge mining to track public health issues in social networks.
Because of the quick growth of technology and the vast amount of data at entirely new scales and complexity-levels, the field needs new tools to face challenges in analyzing big data relevant to a wide range of domains. Her work on developing new graph compression and embedding methods in graph mining to expand large graphs for better management, querying, and display is critical to find meaningful patterns in the enormous size of real-world networks. Dr. Akbas also focuses on developing novel graph and text mining methods to extrapolate useful information from social media data. Her goal is to combat different public health problems, such as opioid addiction and Covid-19, to help government officials and health administrators with providing timely and useful information.