David X. Wu

he/him/his

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