Research Interests
Data Visualization
We study data visualization that focuses on exploring methods to transform complex datasets into intuitive and meaningful visual representations. Modern business topography consists of complicate and intricate mixture of multimodal data, collected from a variety of sources.
We emphasize the development of network visualization systems, designing and building interactive platforms that enable policymakers and business decision makers to make more informed decisions based on network data. We aim to ultimately provide new insights for applications where data-driven design is critical.
Graph Neural Networks
Our lab specializes in the field of Graph Neural Networks (GNNs), one of the most actively developing artificial intelligence (AI) technics in the field of Data Science.
We study to take advantage of the distinctive graph-structure of network data, including link (edge) predictions between the nodes of a graph structure, and pattern recognition from the existing graphs. Our lab incorporates graph structure with data from a variety of domains in order to discover relationships and insights that could not be drawn with traditional methodologies.
Business Analytics
Through empirical research over business domains, our lab study diverse phenomena that arise in the field of business administration and worldwide macroeconomics.
From data collection to causal inference, we study well-structured analytic framework to derive insights that can be applied to business and public decision making. We perform multidisciplinary researches covering business engineering, industrial engineering and innovative business administration.