The Data Science Lab has been dedicated to fundamental research in AI, data science, shallow to deep learning, applied statistics and modeling, and complex intelligent systems 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 AI/DS systems and products;
- Significant real-world X-complexities, X-intelligences, X-informatics, X-modeling, and X-analytics in 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.
The Lab has been working on both general and specific issues related to data science, artificial intelligence, machine learning, and advanced analytics, including
- Data science and intelligent science, covering many mainstream research areas in machine learning, knowledge discovery, artificial intelligence, document understanding, information retrieval, recommendation, and complex systems; and initiating and leading new research areas;
- X-complexities and X-intelligences, involving data complexity/intelligence, behavior complexity/intelligence, domain complexity/intelligence, organizational complexity/intelligence, social complexity/intelligence, economic complexity/intelligence, network complexity/intelligence, human complexity/intelligence, and their meta-synthesis;
- Complex data, such as understanding non-IID data, multi-source and cross-domain data, high-dimensional data, ill-structured data, extremely imbalanced and sparse data, noisy and redundant data, networking data, and outlying data;
- Complex behaviors, including modeling, analysis, prediction, intervention, and active management of individual and group occurring and non-occurring behaviors of humans, living systems and other systems; and
- Complex systems, including modeling, analysis, design, evaluation, and optimization of artificial and natural systems by data science and artificial intelligence.