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 for inference problems, Markov chains, 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. Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
    David X Wu, and Anant Sahai
    Neural Information Processing Systems, 2023
  2. On the Training Instability of Shuffling SGD with Batch Normalization
    David X Wu, Chulhee Yun, and Suvrit Sra
    International Conference on Machine Learning, 2023
  3. Maximum A Posteriori Inference of Random Dot Product Graphs via Conic Programming
    David X Wu, David Palmer, and Daryl R DeFord
    SIAM Journal on Optimization, 2022