We publish, patent, and productize. Our research focuses on robust reasoning, causal inference, privacy-first learning, and human-in-the-loop systems that scale.
A system and method for detecting payment fraud using contextual graph inference combined with human feedback loops to reduce false positives.
Distributed machine learning architecture enabling training on sensitive data without centralized data collection.
Protocol for auditable and interpretable AI models suitable for regulated industries.
Interactive system for detecting anomalies with human expert feedback and continuous model refinement.
Chen, S., Whitmore, J., et al. • Nature Machine Intelligence • 2024
Kapoor, P., Chen, M., et al. • IEEE Transactions on Software Engineering • 2023
Whitmore, J., Chen, S., et al. • ACM Conference on Fairness, Accountability, and Transparency • 2024