In the past decades, our interests have been on exploring X-complexities and X-intelligences by inventing original and actionable theories for X-informatics and X-analytics (where X refers to diverse, both known and unknown).
Specifically, we have involved in many areas related to classic and new-generation artificial intelligence and data science, including knowledge discovery, machine learning, pattern recognition, intelligent systems, behavior informatics, and ubiquitous analytics in diverse domains. Nowadays, we are particularly interested in the following research directions:
- Artificial intelligence and intelligent systems: novel algorithms and systems for knowledge representation, natural language processing and text understanding, optimization, and representation and discovery of X-intelligences; the software engineering and system design of open complex intelligent systems and metasynthetic computing and engineering, involving building large and complex intelligent systems.
- Behavioral, economic-financial, and socio-cultural informatics: original research on informatics and computing for complex behavioral, economic and financial, social and cultural problems; including the modeling and representation, detection and intervention of complex data, events, behaviors, their dynamics and effect in real-time; handling complex couplings, interactions, structures, hierarchies, and heterogeneities in data, behaviors, and problems; understanding their driving intent and sentiment; quantifying their risk, impact, effect, and utility; detecting and predicting occurring and non-occurring behaviors, events and their evolution, significant changes and impact; and providing proactive and actionable solutions for tailored and active management and decision, etc.
- Data science and big data analytics: original research on data science, applied analytics, knowledge discovery, deep learning, and machine learning; deeply understanding complex data characteristics, quantifying data complexities including diversified coupling relationships, interactions, structures, distributions, hierarchies, heterogeneities, dynamics, and their effect in mixed, multisource, multiview, multimodal, and ultrahigh-dimensional heterogeneous data across organizations, markets, and systems, etc.
- Enterprise data science innovation and leadership: impactful enterprise innovation, business transformation, and better practice in data science and analytics for transforming business, government, society, organization, finance, banking, telecom, insurance, education, etc. domains, systems, and environment; inventing and producing actionable big data analytics and machine learning infrastructures, solutions, systems, algorithms and services for enterprise data science and corporate analytics; and fostering data science thinking and leadership in data and intelligence-driven revolution of public and private sectors.
