Works — and earns trust
Two questions decide whether a build lasts: does it work, and can you trust it. We answer both — measurement embedded and real-time so results prove themselves and capacity auto-scales to follow demand, and governance built in from the start: confidential, private, accountable. To a researcher, both are a given, not a feature.
Proven — measured live
Measurement isn't a separate step — it's embedded and real-time. Live telemetry measures each build against its target outcome, and compute auto-scales to follow demand — GPU and capacity stepping up and down with load, no manual re-tooling. The applications are diverse; the rigor underneath is the same.
Governed — by design
Compliance is the goal: confidentiality, integrity, and authentication — the CIA triad — plus accountability. Security, privacy, and the rest are the methods that get you there. We build them in from the start, not bolted on — with depth in generative, agentic, and physical AI explainability and governance, backed by published research.
AI-privacy literacy in Generation Z
Our IEEE study built a framework (DCPS) to measure Gen Z’s AI privacy literacy — and found a stark gap: real awareness of risk, but very low ability to act on it.
Hua & Wang (2025). IEEE TPS-ISA (invited paper).
Clinical AI industrial solutions to data scarcity
Examines two industry answers to scarce clinical data — MONAI’s federated learning and MAISI’s synthetic medical imaging — and maps their risks (inference attacks, HIPAA, fairness) with concrete guidance for safe deployment.
Wang, Kalla & Shadowen (2026). Issues in Information Systems, forthcoming. · Diagram: MONAI Auto3DSeg (Wenqi Li / MONAI project) — Apache-2.0, via Wikimedia Commons.