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Research Fields

1. Human-Computer Interaction and Human Information Behavior

The research area focuses on understanding the interplay among users, technologies, information, and contexts, which aims at investigating the HCI related topics from psychological, cognitive, social, and computational aspects. We adopt multi-level theories and methodologies to explore human information behavior in connection with the use of IT artifacts.


2.Informetrics and Scientific Evaluation

The research area focuses on various aspects of quantitative studies of information and its application in scientific evaluation among which the highlighted research topics are informetrics, scientometrics, webometrics, bibliometrics, and altmetrics.  Both traditional bibliographic data and novel digital data are investigated. We are introducing the cutting-edge technologies like natural language processing and deep learning to better solve questions in this area.


3.Intelligent Informatics

The research area focuses on the edge-cutting theories, emerging techniques and real-world applications regarding intelligent information processing in big data environments, including  automatic and adaptive information harvesting and heterogeneous data fusion, information preprocessing, information indexing and systematizing, information coding and representation, multi-dimensional statistics and evolutional analysis, data mining and information analytics, dynamic social network analysis, information visualization, and relevant systems development and services, etc.


4.Data Analytics and Visualization

The research area focuses on data management, modeling, interpretation, reporting, algorithm development, as well as the state-of-the-art modeling, analysis and visualization techniques. It includes developing increasingly complex data projects and creating models to explore contemporary topics such as finance, energy consumption, online purchases, consumer behavior, fraud, or even disease and epidemics, applying the fundamentals of practical statistics, engineering, math, and data science to complex high dimensional data and open-sourced data sets. And it will emphasize practical challenges involving complex real-world data and include several case studies and hands-on work with the Python/R programming language, etc.


5.Knowledge Management and Knowledge Engineering

The research area focuses on key technologies and applications of knowledge organization, knowledge extraction and knowledge base construction. Research themes include organization and mapping mechanism of multiple domain knowledge based on ontology, knowledge extraction and reconstruction from multi-source and heterogeneous data, construction of designing knowledge base based on multiple domain knowledge, intention mining of designing complex system, modeling of complex system designing and deep learning.