Data mining

Huan Liu

Huan Liu

Prof. Liu’s research focuses on social network analysis, machine learning (ensemble methods, active learning, rule extraction, feature selection and discretization, subspace clustering), data mining (data quality and integration, stream data reduction, bioinformatics, algorithm scaling-up) and real world applications (CRM, Egeria detection in imagery, intelligent driving data analysis, recommender systems). He Liu joined ASU after conducting research in Telecom (Telstra) Australia Research labs. 

Shawn Mankad, UMD Site co-Director

Shawn Mankad, UMD Site co-Director

Prof. Mankad's research focuses on using data analytics to facilitate better decision-making. Specifically, he aims to create and apply data mining, machine learning, and visualization for financial and economic modeling with unstructured and complex structured data. He was a consultant with the U.S. Commodity Futures Trading Commission and also worked at the Federal Reserve Board on characterizing market activity with visual analytic tools. 

Louiqa Raschid, Center co-Director, UMD Site Director

Louiqa Raschid, Center co-Director, UMD Site Director

Prof. Raschid has made pioneering contributions towards meeting data integration, data management, and data mining challenges in multiple domains. She is leading an effort sponsored by the NSF and the CRA Computing Community Consortium (CCC) to develop community financial cyberinfrastructure and to create a data science for finance research. As part of this effort, she collaborates with JPMorgan Chase, State Street, Bank of America, IBM, and  Wells Fargo.

Paulo Shakarian

Paulo Shakarian

Prof. Shakarian specializes in data mining, social network analysis, and cyber security.  His group, the Cyber-Socio Intelligent Systems Laboratory (CySIS) is focused on the  development new of novel techniques to tackle data mining problems relating to socio-cultural systems as well as cyber-physical systems.  Specific focus application areas include social media analytics, law enforcement, malware analysis, deep web mining, and infrastructure protection.

Hanghang Tong

Hanghang Tong

Prof. Tong's research interest is in large scale data mining for graphs and multimedia, with applications to social networks analysis, healthcare, cyber-security and e-commerce. He has published over 80 referred articles and more than 20 patents. He is the associated editor of ACM SIGKDD Explorations and has served as a program committee member in top data mining, databases and artificial intelligence venues. Before joining ASU, he was a research staff menber at IBM T.J Watson Research Center.

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