AI researcher and practitioner — scientist and educator by background. I work on generative models, large language models, and the applied mathematics underneath them, with a bias toward human-centered, world-improving applications.
Currently completing an M.S. in Artificial Intelligence at Johns Hopkins University (Whiting School of Engineering). Before AI, I spent years in space science and the classroom: data scientist on NASA's Kepler mission at the SETI Institute, project staff scientist on NASA's Space Interferometry Mission at JPL/Caltech, and a physics and data-science instructor.
- Researching critic-filtered synthetic data augmentation — using a GAN's own critic as a zero-cost quality filter to make synthetic training data help instead of hurt under sparse-data conditions
- Coursework in LLM theory & practice, generative AI, and modern software engineering
- Looking for research and engineering roles at the intersection of AI, LLMs, and real-world impact
- Portfolio site — research write-ups, including the critic-filtered augmentation study
- The Language of Agents Is Modal and Epistemic Logic — an interactive tutorial on the formal logic of knowledge and belief in AI agents
- professor-claude-ai — an autonomous research-agent prototype built on the Claude API
- chinese-calligraphy-manifold — autoencoder experiments on the structure of Chinese characters
- How Does Gradient Boosting Regression Work? — gradient-boosted trees built from scratch to expose the algorithm's mechanics
- The Logic of Logistic Regression — a tutorial on the mathematics behind logistic regression
- Good Wines, Bad Wines — an end-to-end machine-learning walkthrough classifying wine quality
Graph neural networks · equivariant methods · Bayesian and probabilistic inference · wavelet and multiscale analysis · explainable AI — applied to neuroscience, climate, and astrophysics.