Education:
Brigham Young University, BS, Biostatistics (2020)
Utah State University, Ph.D., Mathematical Sciences: Statistics (2025)
Areas of Interest:
- Human-AI Interaction: Developing collaborative intelligence systems that enable meaningful interaction between humans and AI for enhanced problem-solving.
- Neural Network Interpretability: Understanding how neural networks learn and developing methods for representation learning that provide insights into model behavior.
- Graph-Based Learning: Exploring higher-order interactions and information-theoretic methods for learning on graph-structured data and complex systems.
- Applied Machine Learning: Applying machine learning techniques to domains such as bioinformatics, cybersecurity, genealogy, and other complex systems with real-world impact.
- Open-Source AI Tools: Building accessible AI tools for personal and scientific applications to make advanced machine learning more widely available.
- Generative AI: Designing discrete diffusion models, training-free guidance techniques, and other approaches for controlled generation of structured data.