Q&A with Praveen Rao, Associate Professor of Computer Engineering
Social media has been widely used for political campaigns, marketing and advertising, sharing breaking news, and during catastrophic events. Unfortunately, social media has also become a conduit for cyber threats. University of Missouri-Kansas City faculty member, Praveen Rao, is working to address that.
Rao, an Associate Professor in School of Computing and Engineering, received a prestigious NRC (National Research Council) Research Associateship Senior Fellowship Award to conduct research at the Air Force Research Lab (AFRL) in Rome, N.Y for one year as he investigated ways to detect cyber threats on Twitter.
I conduct research in the areas of big data management and health informatics. My goal is to design scalable systems and techniques for gaining insights from massive, heterogeneous datasets.
Today, companies are using knowledge graphs to improve search results and provide better recommendations to users. Consider a knowledge graph built on Wikipedia and other sources on the Web. It will capture the entities and relationships between them to represent factual information. My research aims to develop scalable algorithms and data structures to enable fast retrieval of information and apply probabilistic inference techniques to draw meaningful conclusions.
How does that connect to your time at the Airforce Research Lab?
Prior to the NRC Fellowship, I spent two summers at the AFRL under the U.S. Air Force Summer Faculty Fellowship. During my NRC tenure, I worked on detecting cyber threats on Twitter using statistical relational learning.
Protecting any organization from cyberattacks has become an extremely important research topic. My work combined machine learning and big data techniques to solve urgent problems faced by society. For example, adversaries post malicious content on Twitter. Innocent users are tricked into clicking links that could lead to the spread of malware and other cyberattacks. Imagine if we could design an algorithm that could detect malicious content and suspicious users on Twitter. My research investigated how statistical relational learning can be used to solve this problem using Twitter data.
What were you hoping to accomplish while you were there?
I worked closely with AFRL researchers, which led to peer-reviewed publications, patent applications and several technical talks. It was a productive sabbatical year. It was also very useful for my PhD student, Anas Katib, who is a co-author on two of the publications.
How will this experience play into your future work?
I continue to work with AFRL researchers on interesting problems in big data analytics and social media. Therefore, I have new projects for my graduate students to work on. It’s important to me that post-graduate students at the SCE are working with real-world projects and are highly engaged with cutting edge research. By continuing partnerships with groups like the AFRL, I am able to bring together problems and bright young minds prepared to solve them.
Any additional details you would like to share?
In addition to detecting cyber threats on Twitter, I am also investigating how statistical relational learning and natural language processing can lead to a promising solution for detecting fake news on Twitter.