Computational Systems
for Scientific Discovery.

Proprietary architectures for autonomous code generation and technical validation.

Scalable ETL systems for processing and validating complex quantum chemistry datasets.

Custom instrumentation for recursive adversarial development and high-throughput analysis.

Investigating the intersection of frontier models and hard science discovery in provenance-locked environments.

Standardized ML model governance addressing serialization fragility and architecture tracking.

Validation framework extending metrology principles to complete ML model management and domain shift detection.

Cryptographic data provenance for drug discovery datasets via real-time SHA-256 verification.

Foundational protocols for cryptographic data lineage, strict validation, and audit certificates.