David X. Wu

he/him/his

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I am a second year Ph.D. student at UC Berkeley, where I’m extremely fortunate to be coadvised by Prasad Raghavendra and Anant Sahai. I’m broadly interested in problems at the intersection of theoretical computer science and statistics, including topics such as computational complexity of statistical inference, Markov chains for sampling, machine learning theory, and optimization. I graduated with a B.Sc. from MIT with a double major in mathematics (Course 18) and computer science (Course 6-3), where I was lucky to do research with Justin Solomon and Suvrit Sra. I’m grateful to be supported by an NSF GRFP fellowship and an OpenAI Superalignment Grant.

selected publications

  1. arXiv
    Locally Stationary Distributions: A Framework for Analyzing Slow-Mixing Markov Chains
    arXiv preprint arXiv:2405.20849, 2024
  2. arXiv
    Fast Mixing in Sparse Random Ising Models
    Kuikui Liu*Sidhanth Mohanty*Amit Rajaraman*, and David X Wu*
    arXiv preprint arXiv:2405.06616, 2024
  1. Robust recovery for stochastic block models, simplified and generalized
    Sidhanth Mohanty*Prasad Raghavendra*, and David X Wu*
    ACM Symposium on Theory of Computing, 2024
  2. Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
    David X Wu*, and Anant Sahai
    Neural Information Processing Systems, 2023