The objective of information systems (IS) is to prepare future leaders in the design, application, and management of computer-based information systems. Students master both traditional concepts and recent advances in techniques, theories, and applications of information systems. Students interested in the organizational aspects of IS can blend their coursework with classes in the applied operations research and management of technology fields. Students can learn about IS design by studying the interaction between humans and computers (HCI), data analytics, information retrieval, and text analytics. IS students can complement their studies with additional technical courses offered by computer science. The skills learned prepare the IS graduate for employment in large or small technology companies, in government or other organizations. Typical positions include those of systems analyst, data scientist, information systems manager, and data architect. Doctoral students are prepared for academic or research positions.
Data and optimization:
We help support sound decisions using data found in various forms and sources. In doing so, we standardize data and apply statistical and optimization methods to provide insights derived from the available data. In order to process data, we use techniques stemming from large-scale data processing, modern database system design, distributed processing using MapReduce and Spark. We consider new big data applications such as data analytics for a sustainable future and educational data mining. Recent projects in this area include: developing policies to price good using real-time data, determining job-shop production scheduling policies.
Search engines and natural language processing:
Our research in this area focuses on the design, analysis, and evaluation of search engines and natural language processing applications. Our research ranges from sentiment analysis to simulating human behaviour with search engines to better predict search effectiveness. Much of our work has an element of human computer interaction (HCI) involved, and we often conduct user studies to better understand how people search and interact with search engines. Often our research involves the application of machine learning for text classification and other purposes. Additional examples of research topics are: information extraction, identification of semantic relations between entities in text, opinion retrieval, and crowdsourcing relevance assessments.
Human-computer interaction (HCI):
We tackle various topics within HCI, including the design and evaluation of novel collaborative systems and games, end-user programming, and interaction techniques for tabletop displays, large-screen displays, smartphones, multi-touch interaction, 3D interaction, and direct vs. indirect pen-input. We draw upon a variety of methods—from designing interactive systems and techniques to using qualitative and quantitative methods to investigate user needs and usability, assess tool adoption, and understand software design processes.