Joy Arulraj is currently interested in the following two fields: Non-Volatile Memory Database Management Systems and Self-Driving Database Management Systems. Please refer to this page for more information.
Xu Chu is interested in data management and machine learning. In particular, he is interested in practical and challenging problems that are in the intersection of these two fields. Example problems include: machine learning for data cleaning and integration, data cleaning for machine learning, training data generation for image and tabular data, automatic feature engineering, and systems for managing machine learning analytics pipelines. Please refer to this page for more information.
Ling Liu is interested in examining performance, availability, security, privacy, trust, data mining, and data management issues in big data systems, cloud computing, distributed computing systems and big data powered deep learning systems. Prof. Liu has been working on various aspects of distributed data intensive systems, ranging from Big Data systems and data analytics, Cloud Computing and cloud datacenters, distributed systems, decentralized and social computing, mobile and location based services, sensor network and event stream processing, to service oriented computing and architectures. Please refer to this page for more information.
Sham Navathe's present research interests include database modeling, design and integration in the context of emerging applications - engineering design, biological (particularly human genome) databases, document and text databases, and collaborative applications. He is also interested in knowledge representation, data mining and knowledge discovery, tools and methodologies for information system design and visualization and user interfaces for better information retrieval. Please refer to this page for more information.
Calton Pu's research interests are in the areas of service computing, distributed and cloud computing, integration and veracity of big data. His current projects include cloud computing (Elba) and big data (GRAIT-DM ). Using experimental data from realistic benchmarks, the Elba project studies the interesting phenomena such as very short bottlenecks that have large impact on n-tier system response time. The GRAIT-DM project collects real world data from social sensors (e.g., Twitter and YouTube) and physical sensors (e.g., USGS GSN and NASA TRMM) to detect physical events and manage real-time information on them.