Conference 2025, Duke University, August, 2025
A training-free acceleration method for generative models, combining a bounded second-order finite-difference extrapolator with a parity-aware reuse schedule. ZEUS achieves near-linear speedups up to 3.64× while maintaining high fidelity, working seamlessly across modalities, models, and schedulers.
AAAI 2026 (Submitted), Duke University, August, 2025
An end-to-end rank-aware streaming inference framework for SVD-compressed large language models. FlashSVD fuses low-rank projection kernels into attention and feed-forward pipelines, avoiding full-size activation buffers and reducing peak activation memory by up to 70.2% without accuracy loss.
NeurIPS 2025 (Submitted), Duke University, May, 2025
A theoretically grounded optimization method that enhances classical coordinate descent by unrolling updates across blocks and applying Taylor-based curvature correction. ECCD achieves up to 13× speedup and maintains sub-10⁻⁵ relative error across logistic and Poisson GLMs, outperforming glmnet, biglasso, and ncvreg on high-dimensional benchmarks.
ICML 2025, Duke University, January, 2025
A training-free diffusion acceleration framework that jointly exploits step-wise and token-wise sparsity via a unified stability criterion. SADA achieves ≥ 1.8× speedup while maintaining LPIPS ≤ 0.10 and FID ≤ 4.5, significantly outperforming prior methods on SD-2, SDXL, Flux, and ControlNet.
Bachelor's Thesis, University of Science and Technology of China, Mathematics Department, May, 2024
This is my bachelor’s thesis. In this thesis, I introduce the concept of the trimmed mean for partially observed functional data, prove the strong consistency of the estimator, and present results from simulation experiments.