Hao Wu 吴昊

William T. Tutte Postdoctoral Fellow
Cheriton School of Computer Science
University of Waterloo

Hao Wu

Biography

I am a William T. Tutte Postdoctoral Fellow at the University of Waterloo, hosted by Florian Kerschbaum. Prior to this, I was a Postdoctoral Fellow at the University of Copenhagen, supported by the Providentia project and affiliated with Basic Algorithms Research Copenhagen (BARC), hosted by Rasmus Pagh. I completed my PhD at the University of Melbourne, advised by Anthony Wirth and Olga Ohrimenko.

Upcoming: In Spring 2027, I will begin a new position as an Assistant Professor in the School of Data Science at The Chinese University of Hong Kong, Shenzhen.

Prospective Students / RAs / Interns

I am happy to hear from motivated and curious students interested in my research. Students with strong mathematical maturity and/or programming skills are especially welcome. Feel free to reach out by email if you are interested in working with me.

Making Privacy Scale

I design privacy-preserving algorithms for large-scale data analysis, aiming for strong utility guarantees while keeping them fast, implementable, and amenable to secure and distributed computation.

Beyond privacy, I have broad interests in algorithm design, particularly sketching algorithms, graph algorithms, and learning-augmented algorithms.

Publications

Grouped by research direction. Also on Google Scholar and DBLP.

Privacy-Preserving Language Pattern Mining

  1. ICDT
    Fast and Optimal Differentially Private Frequent-Substring Mining

    Peaker Guo, Rayne Holland, Hao Wu (alpha order)

    ICDTIn International Conference on Database Theory, 2027

Private Frequency Estimation

  1. PODS
    Optimal Pure Differentially Private Sparse Histograms in Deterministic Linear Time

    Florian Kerschbaum, Steven Lee, Hao Wu (alpha order)

    PODSIn ACM Symposium on Principles of Database Systems, 2026

  2. ICML
    Profile Reconstruction from Private Sketches

    Hao Wu, Rasmus Pagh

    ICMLIn International Conference on Machine Learning, 2024

  3. PODS
    Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data

    Olga Ohrimenko, Anthony Wirth, Hao Wu (alpha order)

    PODSIn ACM Symposium on Principles of Database Systems, 2022

  4. AISTATS
    Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches

    Hao Wu, Anthony Wirth

    AISTATSIn International Conference on Artificial Intelligence and Statistics, 2022

Private Selection

  1. PODS
    Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation

    Rasmus Pagh, Lukas Retschmeier, Hao Wu, Hanwen Zhang (alpha order)

    PODSIn ACM Symposium on Principles of Database Systems, 2025

  2. NeurIPS
    Faster Differentially Private Top-k Selection: A Joint Exponential Mechanism with Pruning

    Hao Wu, Hanwen Zhang (alpha order)

    NeurIPSIn Conference on Neural Information Processing Systems, 2024

  3. ICMLOral
    Tight Data Access Bounds for Private Top-k Selection

    Hao Wu, Olga Ohrimenko, Anthony Wirth

    ICML · OralIn International Conference on Machine Learning, 2023

Learning-Augmented Algorithms

  1. ICLRSpotlight
    Learning-Augmented Frequent Directions

    Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu (alpha order)

    ICLR · SpotlightIn International Conference on Learning Representations, 2025

  2. ICML
    Improved Approximations for Hard Graph Problems using Predictions

    Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu (alpha order)

    ICMLIn International Conference on Machine Learning, 2025

Graph Algorithms

  1. SIGMOD
    Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push

    Hao Wu, Junhao Gan, Zhewei Wei, Rui Zhang

    SIGMODIn International Conference on Management of Data, 2021