Research
Harmony: Adaptive Spatial Intelligence
Human activity is inherently spatial, embodied, and continuous. Yet most AI systems operate on discrete, non-spatial data — creating a fundamental mismatch between computational intelligence and lived experience. Current XR systems suffer from three critical failures: they remain stateless across sessions, isolated within application silos, and unable to adapt intelligently over time.
Harmony investigates a different premise: intelligence emerges from system-level integration, not component optimization alone. A unified spatial intelligence system can enable continuity, adaptation, and generalization across diverse XR experiences — properties impossible in fragmented architectures.
Harmony XR + AI Framework · 2025 · Google Scholar
Harmony One — Four-Layer Architecture
Harmony One implements a closed-loop cognitive architecture. Each layer serves a distinct function, connected through a Shared World Model that persists, evolves, and enables cross-task learning.
Research Questions
How does shared spatial memory affect task performance and learning transfer across XR experiences?
Can a unified system demonstrate measurable improvement in user outcomes over extended interaction timescales?
What quantitative advantages emerge from adaptive guidance compared to traditional static XR instruction?
What architectural and interaction patterns consistently emerge for effective spatial intelligence systems?
Three interconnected research branches validated through Harmony One. Covers multimodal context inference, cognitive load-aware XR interfaces, and explainable AI mediation.
View Research AgendaSelected Publications — APA 7th Edition · View all on Google Scholar ↗
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing
Sensors, 20(3), 690. MDPI, 2020.
X-Reality: Augmented Reality Meets Internet of Things
IEEE INFOCOM Workshops, Honolulu, HI, USA. IEEE, 2018.
An Augmented Reality Facet Mapping Technique for Ray Tracing Applications
Proc. ICDT 2018, Athens, Greece. IARIA, 2018.
Small Teams, Strong Systems
Self-published. 2025. Designing High-Leverage Work for Scaling Teams.
Beginning Windows Mixed Reality Programming (2nd ed.)
Apress / Springer Nature. 2021. ISBN 978-1-4842-7103-2.
Future Directions
Multi-User Intelligence
Shared spatial understanding across simultaneous users
Cross-Environment Transfer
Knowledge portability between distinct contexts
Standardized Benchmarks
Community evaluation frameworks for spatial intelligence
Open Research Ecosystems
Collaborative infrastructure on Harmony principles