Data Analytics & Information Systems
About the Division
This division brings together members from academia, industry and government who share a common interest in topics related to research and practice of data analytics and information systems. The DAIS division is primarily interested in the theory, methodology and practice in all technical areas that develop or apply to data analytics and information systems.
Mission
Become the leading division in IISE by increasing networking and partnership between its members and promoting forums to engage, share and recognize innovative ideas in the field of data analytics and information systems.
Leadership
The DAIS leadership consists of a President, President-Elect, Past-President and 7 Directors. The directors serve a 2-year term. The President serves a 3-year term, 1 year as President-Elect, 1 year as President, and 1 year as Past-President. 2 Student Leader Board members may be appointed to the board for 1 -2 year terms.
Click here to see the current DAIS Board
Honors and Awards
The DAIS division has the following competitions and awards: The Best Student Paper Competition, The Best Track Paper Competition, Data Analytics Competition, Student Mobile App Competition, Professional Achievement Award, and the Teaching Award.
Click here to learn more about each award.
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Research Gallery
One of the benefits of belonging to a professional society is the opportunity to share knowledge about various topics of interest. Browse our research gallery below.
Topic:
Integrating HIV and HPV: A Novel Approach with Hybrid Agent-Based and Compartmental Simulation Methods
Overview: Ms. Xinmeng Zhao. The traditional single-disease model might lead to inaccurate estimations of an intervention's effectiveness, as it fails to consider the syndemic nature of diseases. This oversight is particularly evident in sexually transmitted diseases (STDs) like human papillomavirus (HPV) and human immunodeficiency virus (HIV), where co-infection can compromise the immune response to either virus, or shared behavioral factors influence their transmission.
Recognizing these complexities and existing computational challenges, a new mixed agent-based network and compartmental (MAC) simulation framework has been developed.
Topic:
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
Overview: Mr. Yue Zhao. Machine learning (ML) methods are widely used in manufacturing applications, which usually require a large amount of training data. However, data collection needs extensive costs and time investments in the manufacturing system, and data scarcity commonly exists.
With the development of the industrial internet of things (IIoT), data-sharing is widely enabled among multiple machines with similar functionality to augment the dataset for building ML models.
Click here for more topics.
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