Qiang Zhu
Applications:
Biological Sciences, Engineering, Informatics
Methodologies:
Data Integration, Data Mining, Databases and Data management, Optimization, Security and Privacy
Relevant Projects:

NSF, IBM, Ford


Connections:

ACM SIGMOD; IEEE Computer Society; IEEE SEM; International Society for Computers and Their Applications.

Qiang Zhu

Professor

Computer and Information Science

William E Stirton Professor, Chair, Department of Computer and Information Science and Professor of Computer and Information Science, College of Engineering and Computer Science, The University of Michigan-Dearborn

Dr. Zhu’s group conducts research on various topics, ranging from foundational methodologies to challenging applications, in data science. In particular, the group has been investigating the fundamental issues and techniques for supporting various types of queries (including range queries, box queries, k-NN queries, and hybrid queries) on large datasets in a non-ordered discrete data space. A number of novel indexing and searching techniques that utilize the unique characteristics of an NDDS are developed. The group has also been studying the issues and techniques for storing and searching large scale k-mer datasets for various genome sequence analysis applications in bioinformatics. A virtual approximate store approach to supporting repetitive big data in genome sequence analyses and several new sequence analysis techniques are suggested. In addition, the group has been researching the challenges and methods for processing and optimizing a new type of so-called progressive queries that are formulated on the fly by a user in multiple steps. Such queries are widely used in many application domains including e-commerce, social media, business intelligence, and decision support. The other research topics that have been studied by the group include streaming data processing, self-management database, spatio-temporal data indexing, data privacy, Web information management, and vehicle drive-through wireless services.