• Diffusion model and sampling
• High-dimensional statistical inference
• Algorithm design under budget constraints
• Random matrix theory
• Data-driven decision making

• Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models.
  Yuchen Wu, Yuxin Chen, Yuting Wei.
  

  Preprint, 2024

• Provably efficient posterior sampling for sparse linear regression via measure decomposition.
  Andrea Montanari*, Yuchen Wu*.
  

  Preprint, 2024, Major revision at Journal of the American Statistical Association

• Deep networks as denoising algorithms: sample-efficient learning of diffusion models in high-dimensional graphical models.
  Song Mei*, Yuchen Wu*.
  

  Preprint, 2023, Major revision at IEEE Transactions on Information Theory

• Posterior sampling from the spiked models via diffusion processes.
  Andrea Montanari*, Yuchen Wu*.
  

  Preprint, 2023.

• Statistically optimal first order algorithms: a proof via orthogonalization.
  Andrea Montanari*, Yuchen Wu*.
  

  Information and Inference: A Journal of the IMA (2024).

• Sharp analysis of power iteration for tensor PCA.
  Yuchen Wu*, Kangjie Zhou*.
  

  Journal of Machine Learning Research (2024).

• Fundamental limits of low-rank matrix estimation with diverging aspect ratios.
  Andrea Montanari*, Yuchen Wu*.
  

  Annals of Statistics (2024).

• Integrating water quality data with a Bayesian network model to improve spatial and temporal phosphorus attribution: Application to the Maumee River Basin.
  Zihan Wei, Sarfaraz Alam, Miki Verma, Margaret Hilderbran, Yuchen Wu, Brandon Anderson, Daniel E Ho, Jenny Suckale.
  

  Journal of Environmental Management (2024).

• Theoretical insights for diffusion guidance: a case study for Gaussian mixture models.
  Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei.
  

  The 41st International Conference on Machine Learning (ICML 2024).

• Failures and successes of cross-validation for early-stopped gradient descent in high-dimensional least squares.
  Pratik Patil*, Yuchen Wu*, Ryan Tibshirani.
  

  The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024, Oral, top 7.3% of accepted papers).

• Lower bounds for the convergence of tensor power iteration on random overcomplete models.
  Yuchen Wu*, Kangjie Zhou*.
  

  The 36th Annual Conference on Learning Theory (COLT 2023).

• Adversarial examples in random neural networks with general activations.
  Andrea Montanari*, Yuchen Wu*.
  

  Mathematical Statistics and Learning (2023).

• Streaming belief propagation for community detection.
  Yuchen Wu, Jakab Tardos, MohammadHossein Bateni, André Linhares, Filipe Miguel Goncalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard.
  

  Advances in Neural Information Processing Systems 34 (NeurIPS 2021).

• The estimation error of general first order methods.
  Michael Celentano*, Andrea Montanari*, Yuchen Wu*.
  

  The 33rd Annual Conference on Learning Theory (COLT 2020).

* Alphabetical

Statistical and computational problems in low-rank matrix estimation. Ph.D. thesis, Stanford University, 2023.