The Data Science Lab has been dedicated to fundamental research in applied statistics, data science and AI (DSAI), esp. shallow to deep analytics and learning, and complex intelligent systems and X-Tech for two decades, mainly motivated by

AI thinking & data science thinking with critical `beyond thinking' traits and cognitive paradigms for X-AI and Trans-AI/DS; creative design, statistical, computational and analytical thinking mechanisms and patterns; and original architectures, frameworks, patterns and exceptions for DSAI systems and products;

Significant real-world X-complexities, X-intelligences, X-informatics, X-modeling, and X-analytics in human intelligence, and open complex data, behavior and systems across different disciplines, domains and areas, in particular, natural, physical, social, economic and technical spaces and domains for X-AI and Trans-AI/DS;

Fundamental theoretical gaps and innovation opportunities identified in both existing theoretical systems of statistics, data/intelligence sciences and computing, and to address new and significant theoretical and applied gaps, challenges and problems.

Enterprise data and behaviors grow increasingly bigger and bigger, more and more complex, and more and more valuable. AI, data science and intelligent science, including shallow to deep analytics, learning and modeling, play critical roles in discovering X-intelligences, and data and behavioral value and insights, and in enabling smarter X-Tech, decisions and actions for X-enterprise innovation, productivity transformation, and competitive strength upgrading. The Lab is recognized for its AI/DS leadership in enterprise innovation with high standard and demonstrated impact in assisting major industry and government organizations in building