With the increasing availability of data and computational power at our fingertips, a growing number of organizations and individuals are able to analyze and extract knowledge. Dr. Mohammed Zaki, of Rensselaer Polytechnic Institute, is working to create novel models, algorithms, and systems to enable a more intuitive use and development of data mining and analysis in all areas and especially for applications in bioinformatics and social networks. Dr. Zaki hopes to use his research to make data mining and learning easier for both experts in the Computer Science field as well as non-experts, who are usually professionals with no formal background in Computer Science and Statistics but nevertheless are compelled to analyze huge amounts of data. His research is focused on scalable new methods and novel tools and platforms for coding, mining, and analyzing massive, complex, and interconnected data. The applications of what he terms "intuitive data mining" will allow other researchers in social sciences, biology and life sciences, data journalism, and many other areas to better access, organize and mine relevant information. It will also allow data science experts to reach new advances due to more sophisticated technology.

Dr. Zaki's highly technical research is grounded in real-world applications, especially in bioinformatics, and social and citation networks. For example, in bioinformatics tremendous gains are possible when scientists simultaneously mine the data from genomes, protein structures and networks, metabolic pathways, drug interactions, and numerous other curated datasets. Dr. Zaki is developing novel methods for integrated and holistic mining of omics-scale data to better understand complex systems. Another project on the Frontiers of Science hopes to develop mining techniques to identify ground-breaking ideas and trends, summarize open and solved problems, and display summaries on a timeline by simultaneously mining the graph structure and content of citation networks.

In order to achieve his research goals, Dr. Zaki is directed by three main themes:

• Living in an Interconnected World: To make sense of and to extract valuable insights from the diverse data we need an integrated or holistic modeling and mining approach. Dr. Zaki is developing novel techniques for integrated graph analytics. His tools will allow experts and non-experts to makes sense of the incredible amount of information produced by others to better inform their own work.

• Rise of Complex Big Data: Massive amounts of data are being collected in different disciplines ranging from the natural and physical sciences to the social sciences and digital humanities. Dr. Zaki is scaling his existing research in order to mine and learn from big data. By building scalable and parallel data mining and graph mining algorithms, Dr. Zaki and his team are leveraging state-of-the-art hardware and architectural trends, such as many-core processors and cloud computing, to work with big data sets in a reasonable amount of time.

• Data Analytics for the Masses: There is an increasing gap between the scale and complexity of data and the tools that currently exist to mine them for knowledge. Dr. Zaki is focusing on intuitive data mining methods and frameworks that will address the necessity for improved tools for data science.