# Shane Cairns - Personal & Professional Portfolio

> Shane Cairns is a Ph.D. researcher in Computer Science at Missouri S&T (based in St. Louis, MO) focused on continual learning, representation learning, adaptive neural systems, and adversarial ML. First-author publication at IJCNN 2026 with two follow-up manuscripts in submission. Seeking AI research and engineering roles in defense and AI safety.

## Site Structure

- /work: Full resume: research experience, publications, education, industry experience, skills
- /life: Personal story with a chronological timeline, an interactive jungle explorer game, and photo galleries
- /llms-full.txt: Complete resume in markdown
- /Shane_Cairns_Resume.pdf: One-page resume (PDF)

## Contact

- Email: syre@duck.com
- Location: St. Louis, MO
- LinkedIn: https://www.linkedin.com/in/shane-c-b5b948135/
- GitHub: https://github.com/syre-ai

## Research Interests

- Continual and lifelong learning
- Representation learning (vector-quantized, prototype-based, discrete-continuous latents)
- Adaptive neural systems and Adaptive Resonance Theory (ART)
- Generative modeling on learned discrete representations
- Adversarial robustness of incremental learners
- AI safety and trustworthiness

## Publications

- Cairns, S., et al. (2026). "Robustness of Fuzzy ARTMAP to Adversarial Attacks and Progressive Adversarial Training for Streaming Learning." IJCNN 2026. Extended version: https://arxiv.org/abs/2605.06902
- Cairns, S., Wunsch, D.C., & Liu, J. (2026). "ART-VQ: Vigilance-Controlled Projection Quantization for Discrete-Continuous Latent Representations." In submission.
- Cairns, S., Wunsch, D.C., & Liu, J. (2026). "Generating from Regions: Modeling Discrete Hyperbox Identities and Continuous Within-Region Positions." In submission.