CS student and ML engineer at UW — working on sparse autoencoders, SSL representation theory, and mechanistic interpretability
S² Representation Geometry · alignment vs uniformity · drag to rotate
I'm a Computer Science student at the University of Washington's Paul G. Allen School of Computer Science & Engineering, maintaining a 4.0 GPA while doing research in mechanistic interpretability and representation theory.
Currently I'm working as an ML Research Engineer at a stealth AI startup, investigating how CNN inductive biases shape SSL representation geometry — diagnosing alignment, uniformity, and dimensional collapse across architectures.
I was previously a Research Fellow at Algoverse AI, where I published RT-TopKSAE at ICLR 2026 — adapting the rotation trick to TopK sparse autoencoders, achieving 100% dictionary utilization vs. the baseline's 47%.
ML Research Engineer
Research Fellow · Remote
Paul G. Allen School of Computer Science & Engineering
Bachelor of Science in Computer Science
4.0 GPA
Dean's ListDesigned a VAE enforcing equivariance in the latent space to ensure geometric transformations in input frames correspond to predictable latent trajectories, improving temporal consistency in generated video.
Derived and implemented a full numerical linear algebra engine in pure NumPy — Gram-Schmidt QR factorization, power iteration with deflation, and two-sided Jacobi SVD. Validated against scikit-learn with residuals under 10⁻¹⁰.