Production data pipelines, AlphaZero self-play reinforcement learning, and Bayesian capability modeling.
Detecting strategic sandbagging and capability concealment in evaluation-aware frontier LLMs through Bayesian game engines, joint HMM belief tracking, and pulsed undercover interventions.
Teaching a machine to think through chess. An experimental research environment exploring how AI systems learn through self-play, neural evaluation and search.
Active learning loops, posterior parameter estimation, and sequential experiment tracking. Inferring latent model capability bounds under resource-constrained evaluation.
High-throughput distributed event streaming, real-time schema evolution guardrails, and analytical lakehouse architecture built for zero-downtime data pipelines.
Production data pipelines for educational and labor institutions: automated schema drift alerts, zero-loss raw lake staging, and semantic vector indexing without brittle scrapers.
