The Master of Science in Information Quality (IQ) at 糖心Vlog传媒 Little Rock is the first university-based program to offer a graduate degree Information Quality. The program is fully designed for those who want to be data leaders and want to go onto world鈥檚 most prestigious data industries as Data Scientist, Data Analyst, Data Steward, IQ Manager, Product Manager, Project Manager, Chief Data Officer and Director of Data Governance.

Dr. Richard Y. Wang is Director of MIT Information Quality (MITIQ) Program at the  Massachusetts Institute of Technology and has been instrumental in the establishment of the Master of Science in Information Quality degree program at the University of Arkansas at Little Rock (25 students enrolled in the first offering in September 2005.  He also holds an appointment as University Professor of Information Quality, University of Arkansas at Little Rock.

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MS Information Quality Program

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, a graduate student in the Information Quality Graduate Program at the University of Arkansas, Little Rock, was selected to attend the Computing Research Association (CRA) Graduate Cohort for Underrepresented Minorities and Persons with Diabilities () workshop held in Austin, Texas, March 5-7, 2020.

Adeola who is currently in the MSIQ program participated in the prestigious program which is aimed at preparing graduates students for careers in the computer science industry. The mission of the workshop is to enhance innovation by joining with industry, government and academia to strengthen research and advanced education in computing.

The third annual CRA-URMD Workshop attracted students from schools across the United States and Canada. CRA covered the travel expenses for all selected students that collectively represent a diverse set of computing-related research areas and institutions.

At the workshop, students spent three days interacting with senior computing researchers and professionals, who shared pertinent information on graduate school survival skills, as well as more personal information and insights about their experiences. The workshop also included a mix of formal presentations and informal discussions and social events.

According to Adeola, attending the Grad Cohort for URMD enabled her to build mentoring relationships and developed peer networks with other students from institutions across the United States and Canada and as well professionals from IBM, Google, Microsoft, Oak Ridge Research and Bloomberg respectively.

“I was able to learn about professional and personal development, how to make a decision to enroll into a PhD program, and balancing graduate school with other aspect of my lives.” Adeola explained. “This workshop also exposed me to the various research that other students are doing and provided insights on how I can start developing my research projects”, she stated referring to the poster session where students present their research and received feedback.

Adeola is a volunteer researcher at the Entity Resolution and Information Quality (ERIQ) Lab assisting with the regression testing of the OYSTER software, an entity resolution tool developed by IQ students.

For more information on the CRA URMD Grad Cohort Workshop visit .

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Xinming Li, a doctoral candidate in the Computer and Information Sciences PhD program, led a team to win First Place in the Graduate Student Division of the 2020  (AR-BIC) poster contest. The team included Ting Li, a doctoral candidate in the Bioinformatics program and faculty advisor Dr. John R. Talburt, Acxiom Chair of Information Quality at the University of Arkansas at Little Rock. The AR-BIC Conference is an annual event sponsored by the , and this year鈥檚 event was held on February 10-11, 2020 at the Wyndham Riverfront Little Rock. The winning poster titled 鈥淩eferenceToVec: Teach the Machine to Read People References鈥� was in line with the conference theme focused on artificial intelligence (AI) research and applications in Arkansas.

The poster described a new method for embedding textual references to persons such as customers, patients, students, into numeric vectors suitable for machine learning models for entity resolution. The entity resolution process determines whether two references are referring to the same person, or to different persons. Entity resolution is a key factor in determining the accuracy of data integration and data analytical processes, and is still a new area for the application of AI and machine learning.

The Arkansas Bioinformatics Consortium (AR-BIC) is comprised of the major research universities in Arkansas (including Arkansas State University, University of Arkansas, University of Arkansas at Little Rock, University of Arkansas for Medical Sciences, and University of Arkansas at Pine Bluff) plus the U.S. Food and Drug Administration鈥檚 National Center for Toxicological Research (NCTR) near Pine Bluff, Arkansas.

AR-BIC 2020 keynote and workshop speakers addressed the basic concepts and methodologies of AI, along with describing their advanced applications in biomedical research, food safety, drug discovery and development, and clinical settings. In addition, the consortium provided a networking and collaboration platform for the participants wanting to learn more about AI. A video of the poster competition can be found at 

The Information Science Department at the University of Arkansas Little Rock offers graduate certificates in data science and information quality, and master of science and doctoral degrees in both information quality and information science. All of these programs are offered online and on-campus. The MS in IQ and in IS were ranked 19th best in the nation by US News and World Report.

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