Research
Peer-reviewed, published, and open to explore.
50+ peer-reviewed publications, preprints, and refereed presentations. Below is a selection most relevant to our technology; click through to read the papers.
Digital twins
Person-specific models of physiology, stress, and performance.
Engineering mental wellness: A digital twin for chronic stress modeling and real-time intervention
MODSIM World 2025 · Paper No. 77
Best Paper AwardRead paper →
Physiological digital twins for multi-crew training optimization
I/ITSEC 2026 · Orlando, FL
Accepted · Dec 2026
Personalized EEG-driven digital twin models for AI- and VR-based language therapy in children with autism spectrum disorder
MODSIM World 2025 · Paper No. 80
Human digital-twin-style models for early deterioration prediction in austere trauma care: A validation-readiness simulation study
MODSIM World 2026 · Emerging Capabilities Track
Adaptive self-regulation in digital twin therapy: Modeling the development of autonomous emotion regulation skills through human–AI collaborative learning
Lecture Notes in Computer Science (Springer), 2026
A digital twin framework of motor performance variability and fatigue trajectories under cumulative sleep debt
HFES Annual Meeting (ASPIRE 2026)
Accepted
Physiological sensing & human–AI teaming for defense
Operator-state detection, cognitive load, and adaptive training.
Dual-modal stress detection for adaptive autonomous wingman teaming
I/ITSEC 2026 · Presented with live demo at DAFMSAS 2026
Accepted · Dec 2026
Emotion-aware cognitive load management in human–AI teaming for defense operations
MODSIM World 2025 · Paper No. 63
Enhancing decision-making under pressure: Simulating cognitive load in high-stakes training
I/ITSEC 2025
Adaptive simulation-based training for military decision-making: Leveraging IoT-derived cognitive and emotional feedback
MODSIM World 2025 · Paper No. 82
Affective state modeling to predict training dropout in soldier academies
MODSIM World 2025 · Paper No. 64
Emotion-aware autonomous vehicle control: A framework for driver state management through adaptive AI-driven interventions
ANNSIM 2025 · Madrid, Spain
Published
Trust & transparency in AI
How AI can explain itself in ways that strengthen human judgment and trust.
The transparency dilemma: Reconciling trust calibration with perceived autonomy through personality-adaptive AI systems
Lecture Notes in Computer Science (HCII 2026)
Best Paper AwardRead paper →
The transparency paradox in explainable AI: A theory of autonomy depletion through cognitive load
arXiv preprint, 2026
When to trust the machine: A simulation framework for human–AI collaboration
IHSI 2026 · AHFE International
Autonomy in transition: AI, self-identity, and the evolution of human agency
Proceedings of the HFES Annual Meeting, 2025
Let's work together
For government program managers, prime contractors looking for teaming partners, and research collaborators.