AI & Data Science Research

  The Data Science Lab has been dedicated to fundamental research in data science and complex intelligent systems over a decade, mainly motivated by

• Data science thinking including scientific cognitive paradigms and thinking patterns, creative design thinking and mechanisms, analytics and learning architectures, frameworks and design patterns;
• Significant real-world complexities, challenges and intelligences in open complex data, behavior and systems, identified in different domains and areas, in particular, public sector, business, finance, online and living societies, core industries, and socio-economic 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 team has been working on both general and specific issues related to artificial intelligence, data science, machine learning, and advanced analytics, including

• Data science and intelligent science, covering many mainstream research areas in artificial intelligence, machine learning, knowledge discovery, 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; and
• Complex behaviors, including modeling, analysis, prediction, intervention, and active management of individual and group behaviors of humans, living systems and other systems.

Enterprise Data Innovation

Enterprise data and behaviors grow increasingly bigger and bigger, more and more complex, and more and more valuable. Data science and intelligence science have played critical roles in data-driven discovery of X-intelligences, value and insight and in recommending smarter decision-making actions for enterprise innovation, productivity transformation and competitive strength upgrading.   Thanks to the partnership and support, our team has been well recognized for its leadership and best practice in enterprise data science, high standard and demonstrated impact in assisting major industry and government organizations in building

• the thinking and foundation to design, implement, manage, review and optimize enterprise data science innovation decision-making, plans, policies, mechanisms and specifications;
• the competencies and skills to create, undertake and optimize enterprise data science infrastructure, systems, models, case studies, and practice;
• the qualifications for next-generation data science professionals through offering high quality Master's/doctoral courses and corporate workshop/training to undertake and lead actionable enterprise data science.