Applied AI / learning systems / responsible digital transformation
I am Yun-Cheng Tsai, Ph.D., an AI and machine learning scholar based in the Seattle area. My work sits between AI research, university teaching, learning analytics, explainable decision support, and public AI education.
I am less interested in AI as spectacle than in systems people can understand, teach, govern, and improve. Across university appointments and applied projects, I have focused on helping institutions move from AI concepts to rigorous, interpretable, and socially useful implementation.
Beginning July 2026, I serve as Seattle Regional Consultant & Local Implementation Lead for Future Intelligence Hub Seattle, by invitation of the Global ESG Leadership Organization.
Professional profile
My career has moved across computer science, financial technology, quantitative modeling, university teaching, and educational innovation. I bring a builder's mindset to AI: the work is not complete when a model runs, but when people can understand its assumptions, evaluate its limitations, and apply it responsibly in real contexts.
This perspective shapes my teaching and public work. I design AI learning experiences for students, educators, professionals, and organizations that need more than surface-level tool use. The goal is durable capacity: people who can reason with data, build with AI, explain decisions, and use intelligent systems with judgment.
Current public role
I serve as Seattle Regional Consultant & Local Implementation Lead, supporting local implementation of the Future Intelligence Hub framework in the Seattle region.
This role is by invitation of the Global ESG Leadership Organization.
Research and implementation agenda
The through-line is practical: AI should produce systems people can evaluate, teach, govern, and use in real institutional settings.
Designing AI learning experiences that help students, educators, and professionals move from tool use to reasoning, implementation, and reflection.
Building and studying AI systems that make decisions easier to inspect, challenge, and improve in healthcare, finance, and institutional workflows.
Translating AI expertise into public programs that strengthen digital responsibility, youth capability, and institutional trust.
Academic teaching and program leadership
Associate Professor, Department of Technology Application and Human Resource Development.
Associate Professor, School of Big Data Management.
Assistant Professor, Center for General Education.
Predoctoral Research Fellow, Berlin, Germany.
Students should learn to direct AI, critique its limits, justify assumptions, and explain design choices. A system is not finished simply because code runs; it is finished when it can be understood and improved.
As co-PI and collaborative designer of NTNU's Educational Big Data Micro Program, I helped build a pathway from foundational coursework to analytics, MVPs, proofs of concept, and industry-facing projects.
My students have produced award-winning hackathon, capstone, programming, AI motion analysis, and industry collaboration outcomes, including an Evaluation Cloud system for long-term care workflows.
Scholarship and source of truth
Publication lists, citation counts, and indexing details change over time. This site summarizes stable research directions and links out to maintained academic records for the live publication profile.
Current publication list, citations, coauthor records, and paper indexing are best viewed directly on Google Scholar.
Open Google ScholarProfessional background, public roles, collaborations, and current profile details are maintained on LinkedIn.
Open LinkedInAdvisory and public engagement
Alongside academic work, I support practical AI education, responsible digital capability, and project-based learning for schools, professionals, civic groups, and community-facing initiatives, including the local implementation work of Future Intelligence Hub Seattle.
Workshops and program design for youth, educators, and organizations navigating AI use, online trust, misinformation, digital wellbeing, and ethical technology practice.
Structured learning experiences for learners and teams who need to understand AI systems, data workflows, prompt-based tools, and evidence-based implementation.
Selective project-based mentoring remains available, but it is framed as one expression of a broader research-informed AI education and capacity-building practice.