Research
Making embeddings easier to trust.
Lab work, personal questions, and method notes around distortion, long-task memory, and reliable model behavior.
Latent Structure Lab
Fixing distortion in low-dimensional visualization
My main research continues the published work on detecting distortion in nonlinear data embeddings. Our current goal is to reduce that distortion with algorithms, so low-dimensional visualizations better preserve the high-dimensional structure they represent.
Personal Research Questions I’m Interested In
Multi-source hallucination
How can we reduce hallucination when a model must reconcile multiple input sources?
Long-task memory
How should long-short memory systems be designed when tasks are long but context windows are limited?
Embedding reliability
How can we quantify embedding reliability across random seeds and data shifts?
Things I’m Learning
LLM recommendation and voting
I have researched recommendation systems with large language models, including LLM + vLLM majority voting to improve answer accuracy.
Past Publications & Posters
Publication
Interactive Visualization of Metric Distortion in Nonlinear Data Embeddings using the distortions Package
Briefings in Bioinformatics, 2026 · DOI: 10.1093/bib/bbag136 · Full text available on PMC