At RAM Labs I worked across tactical cybersecurity, code vulnerability modeling, distributed edge inference, and cognitive security.
Core role
My responsibilities included modeling, data pipelines, evaluation, and integration.
Program snapshots
- T-UEBA: tactical UEBA for Zero Trust conditions using temporal-heterogeneous behavior modeling and adaptive risk calibration.
- DL-PATCHER: automated vulnerability repair workflows over code corpora with transformer-family modeling and curation-heavy fine-tuning.
- SpHyRE-Net: low-SWaP anomaly-focused modeling for constrained edge environments.
- DAICON: distributed AI execution over heterogeneous tactical nodes with resilience under ad hoc networking constraints.
- DEVIS: embedded vulnerability detection in binaries with ML-assisted path prioritization.
Evaluation
These programs required testing model behavior under changing data, limited compute, and unreliable connectivity.
Patent context
Co-inventor: Automated Bug Fixing Using Deep Learning Including Pre-training and Fine-tuning, U.S. application 18/375,839 (Geddes, Mabie, McGraw).