David X. Wu



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). I’m grateful to be supported by an NSF GRFP fellowship.

selected publications

  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
  3. On the Training Instability of Shuffling SGD with Batch Normalization
    David X Wu*Chulhee Yun, and Suvrit Sra
    International Conference on Machine Learning, 2023