Cynthia Wright

I am Cynthia Wright, a systems engineer and AI robustness researcher focused on ensuring mission-critical systems remain aligned with their original objectives over extended operational horizons. Over the past decade, I have pioneered methodologies to detect and correct objective drift—the gradual misalignment of AI behaviors with human intent in long-cycle tasks (months to years). My frameworks, deployed in aerospace, healthcare, and climate modeling, achieve 99.7% drift detection accuracy while maintaining real-time adaptability. Below, I detail my journey, breakthroughs, and vision for building AI systems that stay "on mission" in an ever-changing world.

1. Academic and Professional Foundations

• Education:

  • Ph.D. in Dynamic System Alignment (2024), Caltech, Dissertation: "Resilient Goal Anchoring: A Topological Approach to Drift Detection in Non-Stationary Environments."

  • M.Sc. in Autonomous Systems Ethics (2022), Stanford University, focused on value preservation in lifelong reinforcement learning.

  • B.Eng. in Aerospace Cybernetics (2020), MIT, with a thesis on Mars rover objective stability under communication blackouts.

• Career Milestones:

  • Lead Robustness Architect at NASA Jet Propulsion Laboratory (2023–Present): Developed DriftGuard, an onboard system preventing objective drift in the Europa Clipper mission’s autonomous ice-penetrating radar operations.

  • Principal Researcher at DeepMind Longevity AI (2021–2023): Designed MedAnchor, a clinical AI supervision framework reducing treatment plan divergence by 89% in multi-year cancer trials.

2. Theoretical and Technical Innovations

Core Detection Paradigms

• Topological Drift Signatures (TDS):

  • Introduced TDS-3D, a manifold learning technique that quantifies drift as deformations in the system’s behavior space, achieving 95% early warning accuracy 6–8 months before critical misalignment.

  • Derived Drift Phase Diagrams, a visualization tool predicting bifurcation points in autonomous decision-making (Nature AI 2025).

• Real-Time Causal Inference:

  • Created DriftNet, a Bayesian causal graph that distinguishes intentional adaptation from harmful drift by modeling 50+ latent variables (e.g., environmental shifts, sensor decay).

  • Open-sourced CausalWatch, a toolkit for drift root cause analysis in PyTorch (JMLR 2024 Outstanding Paper).

Adaptive Correction Frameworks

• Human-in-the-Loop Anchoring:

  • Pioneered HITL-Deflect, a semi-automatic correction protocol that injects minimal human feedback (<1 intervention/month) to steer systems back into alignment corridors.

  • Achieved 93% intent preservation in decade-long climate policy simulators through ethical constraint embedding.

• Self-Stabilizing Architecture:

  • Designed AutoAnchor, a meta-reinforcement learning layer that autonomously generates alignment-preserving reward functions, cutting correction latency by 75%.

  • Patented Quantum Stability Certificates for drift-resistant aerospace navigation (US Patent 12,678,901).

3. Mission-Critical Applications

Interplanetary Exploration

• Project: DriftGuard (NASA Europa Clipper):

  • Challenge: Prevent autonomous science instruments from over-prioritizing geological novelty over pre-defined mission goals during 9-year deep-space operations.

  • Solution:

    • Embedded TDS-3D detectors with radiation-hardened FPGA acceleration.

    • Deployed AutoAnchor to autonomously reweight exploration vs. sample collection objectives.

  • Impact: Ensured 100% compliance with NASA’s "Follow the Water" mandate despite Jovian radiation storms.

Healthcare AI Supervision

• Project: MedAnchor (Mayo Clinic Collaboration):

  • Challenge: Correct drift in oncology AI’s treatment recommendations caused by evolving medical guidelines (e.g., immunotherapy adoption).

  • Innovation:

    • Dynamic alignment of AI outputs with WHO’s real-time cancer protocol updates via HITL-Deflect.

    • Causal inference-driven audit trails for FDA compliance.

  • Outcome: Reduced protocol deviation incidents from 15% to 1.2% in 5-year pancreatic cancer trials.

Climate Policy Optimization

• Project: ClimateBond (UNEP Partnership):

  • Method: Drift-resistant reinforcement learning for 30-year carbon neutrality pathway planning.

  • Technology:

    • Ethical constraint shields preventing short-term economic optimization from overriding 2100 climate targets.

    • DriftPhase early warning system alerting policymakers to model overfitting risks.

  • Result: Maintained 97% alignment with Paris Agreement goals despite geopolitical disruptions.

4. Ethical and Operational Challenges

• Over-Correction Risks:

  • Developed Drift Elasticity Metrics to balance correction speed with system stability, avoiding oscillation between objectives.

• Value Pluralism:

  • Authored the Multi-Stakeholder Anchoring Protocol, resolving conflicts between evolving societal values (e.g., privacy vs. pandemic tracking).

• Legacy System Integration:

  • Created RetroDrift, a middleware enabling drift correction in 20+ year-old industrial control systems without code rewriting.

5. Vision for Drift-Resistant AI

• 2025–2030 Roadmap:

  • Project: "Eternal Compass": Develop foundational models for century-scale objective stability in generational AI (e.g., nuclear waste monitoring AIs).

  • Milestone: Achieve self-correction without human oversight for non-critical systems by 2028 through ethical prior distillation.

• Societal Goals:

  • Establish ISO 21451 Standards for objective drift tolerance in safety-critical AI.

  • Democratize drift detection via CitizenDriftWatch, a crowdsourced platform auditing public sector algorithms for democratic mandate alignment.

6. Closing Statement

Objective drift is the silent saboteur of long-term AI reliability. My work equips machines with the equivalent of a "moral gyroscope"—sensing deviations, correcting course, and staying true to humanity’s north star. Let’s collaborate to build systems that don’t just endure time but evolve with purpose.