Kevin Dombkowski

By | | No Comments

Kevin Dombkowski, DrPH, is Research Associate Professor in the department of Pediatrics in the University of Michigan Medicine. Dr. Dombkowski, also holds a second appointment in the School of Public Health.

Kevin’s┬áprimary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.

Further, his research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services, including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.

Qiang Zhu

By | | No Comments

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.

zhu-image

Using a data-partitioning based index tree (the ND-tree) to find sequences that are similar (with distance 1) to a given query sequence from a large sequence database in a Non-ordered Discrete Data Space (NDDS).