TEACHER · ENGINEER · ENTREPRENEUR · RESEARCHER
From ePuppy to Causal AI—exploring how AI understands people and complex systems.
I am a teacher first, and a hands-on engineer who brings research questions into real products. This is not a résumé list; it is the story of how teaching, entrepreneurship, public R&D and academic research became one continuous journey.
ONE CONTINUOUS JOURNEY
From connected devices and electronic pets to AI teaching, multimodal interaction and causal inference, each stage asks how technology can understand context and become a usable system.
CONNECTED PRODUCT
GOVERNMENT R&D
PLATFORM DELIVERY
TEACHING & RESEARCH
EPUPPY ARCHIVE
The early products explored speech, multiple languages, email reading, Bluetooth, Internet calling and care scenarios. The limit was not the idea, but the computing, sensing and language technology of the time.
TWO RESEARCH DIRECTIONS
One track studies human–AI interaction; the other studies markets formed by human decisions. Both involve time, multiple data sources, dynamic systems and causal reasoning.
Research direction
Can an AI companion learn emotional and situational context from voice, expression, motion, physiological and environmental signals?Affective Computing × Multimodal Sensor Fusion × LLM × Embodied AIConsent, data minimization, de-identification and safe interaction come first. A research concept is not presented as a finished product.
Research direction
Does an accurate prediction model actually understand why markets change?Econometrics × Time-Series Transformer × Causal InferenceThis research compares explanatory methods. It is not investment advice and offers no performance guarantee.
Causal & Multimodal AI for Human and Complex Systems—using AI to understand people and the complex systems people create.
TWO DISCIPLINES
Management research helps explain organizations, decisions and complex systems. Electrical engineering deepens work in sensing, models, devices and human–machine interaction.
Completed doctorate connecting management, decisions, nonlinear systems and technology applications.
Admission is confirmed. This is a new electrical-engineering research program, not a second business doctorate.
FROM IDEA TO IMPACT
Teaching, research and industry collaboration follow the same basic rhythm, with reviewable results at every stage.
Clarify users, context, constraints and the real problem.
Turn the problem into hypotheses, methods and evidence.
Test technical, data and risk assumptions at minimum scope.
Connect the model to an interface, process, device or platform.
Evaluate engineering, production, operation and transfer conditions.
Check whether learning, users or organizations actually improve.
PUBLIC REFERENCES
Only public, review-appropriate links are listed. Private CVs, proposals, pricing, contracts, archives and confidential collaboration materials remain internal.
LEARN OR BUILD TOGETHER
Students should begin with the course directory. Schools, companies and research teams can share a non-confidential problem statement so we can assess the right depth of collaboration.