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Research group We are developing advanced algorithms and models to find patterns, anomalies, and causality. This allows us to predict outcomes and facilitate decision-making even in very large and complex datasets.
The research group of Causal Intelligence and Deep Data Mining was established to develop algorithms and implement prototypes for multi-sources heterogeneous information federation and privacy preservation on multimodal data. Our main research interests include data federation and privacy protection by applying techniques for text mining, natural language processing, machine learning, semantic web, and causal discovery.
Regarding datatypes to integrate, we consider data from structured (e.g, records in DB), semi-structured (e.g, XML, JSON) and unstructured sources (e.g, news, social media). In a broad view of the core techniques, our group applies technologies of database, data mining, natural language processing, machine learning, and ontology based semantic web technology.
As application-driven research, we aim to realize general data integration framework to adapt multiple applications (e.g, information retrieval, recommendation systems, online advertisements) and meanwhile acquire the unique characteristics of domain-data to boost the integration accuracy and AI trustworthiness on specialized domains (e.g, healthcare, social networks, robotic heavy machine, demographic, review data).
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