David X. Wu

he/him/his

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I am a 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, such as Markov chains, statistical inference, and machine learning theory. Last summer, I was an ML research intern at Windsurf working on coding agent capabilities and evals. I’m grateful to be supported by an NSF GRFP fellowship and an OpenAI Superalignment Grant.

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. As an undergrad, I interned in quant finance at HRT and Akuna Capital.

Selected publications

    1. Weak Poincaré Inequalities, Simulated Annealing, and Sampling from Spherical Spin Glasses
      ACM Symposium on Theory of Computing, 2025
    2. ICLR
      Provable Weak-to-Strong Generalization via Benign Overfitting
      David X Wu, and Anant Sahai
      International Conference on Learning Representations (Preliminary version at NeurIPS’24 M3L Workshop), 2025
    3. Locally Stationary Distributions: A Framework for Analyzing Slow-Mixing Markov Chains
      IEEE Annual Symposium on Foundations of Computer Science, 2024
    4. 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
    5. Robust recovery for stochastic block models, simplified and generalized
      Sidhanth Mohanty*Prasad Raghavendra*, and David X Wu*
      ACM Symposium on Theory of Computing, 2024