Aaron W. Storey
PhD Candidate | Explainable AI Researcher | Opening the Black Box

Research Focus
Making AI interpretable, trustworthy, and auditable.
| Area | Focus |
|------|-------|
| XAI Methods | Attribution, counterfactuals, faithful explanations |
| Transformer Interpretability | Logic-gated modules, attention probes, mechanistic analysis |
| Adversarial ML | Robustness testing, perturbation analysis, model brittleness |
| Applications | Biometrics, LLMs, vision models, safety-critical systems |
PhD Thesis: Developing falsifiable attribution methods for explainable AI - systems that can be empirically validated and audited.
Proposal Defense: February 2026 @ Clarkson University
Featured Projects
| Project | Description | Status |
|---------|-------------|--------|
| Beta Regression Framework | Statistical framework for bounded biometric performance in child face recognition | IEEE T-BIOM |
| Agentic AI Seminar | Personas & affective prompting as behavioral control surfaces | Clarkson Seminar |
| Goblin Forge | Multi-agent CLI orchestrator for Linux with tmux isolation | Active Dev |
| SIFTER | NASA Space Apps 2024: ML seismic detection for moon/marsquakes | NASA Hackathon |
| 100 Days of ML | Complete 35-lesson curriculum: Python basics to XGBoost |
|
| Research Assistant | PRISMA 2020 + NIH-compliant research workflow automation | 22 skills, 10 agents |
| Money Talks | Trading & investing education: 100 notebooks, 5 classes | Complete |
| EE622: Biometrics Transformers | 10-week graduate course: ViT for fingerprint, face, gait, speaker, ECG | Clarkson University |
Current Work
- Dissertation: Counterfactual frameworks for falsifiable attribution in face verification
- IEEE T-BIOM (Under Review): Beta regression for bounded biometric metrics
- IEEE T-BIOM (In Preparation): AI Act/GDPR/Daubert β XAI validation framework
- MDPI Electronics (Under Review): Hardware security review for embedded processors
- In Preparation: Affective prompting & persona manipulation systematic review
- AI Engineer @ Kymera Systems: Building intelligent AI orchestration systems
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"Make explainability an engineering discipline: measurable faithfulness, human factors by design, and governance that withstands audits."