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

Privacy-Preserving Language Pattern Mining

[ICDT] Fast and Optimal Differentially Private Frequent-Substring Mining

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

In International Conference on Database Theory, 2027

Private Frequency Estimation

[PODS] Optimal Pure Differentially Private Sparse Histograms in Deterministic Linear Time

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

In ACM Symposium on Principles of Database Systems, 2026

[ICML] Profile Reconstruction from Private Sketches 

Hao WU, Rasmus Pagh

In International Conference on Machine Learning, 2024

[PODS] Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data  

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

In ACM Symposium on Principles of Database Systems, 2022

[AISTATS] Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches 

Hao WU, Anthony Wirth

In International Conference on Artificial Intelligence and Statistics, 2022

Private Selection

[PODS] Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation

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

In ACM Symposium on Principles of Database Systems, 2025

[NeurIPS] Faster Differentially Private Top-k Selection: A Joint Exponential Mechanism with Pruning 

Hao WU, Hanwen Zhang (alpha order)

In Conference on Neural Information Processing Systems, 2024

[ICML, Oral] Tight Data Access Bounds for Private Top-k Selection

Hao WU, Olga Ohrimenko, Anthony Wirth 

In International Conference on Machine Learning, Oral, 2023

Learning-Augmented Algorithms

[ICLR, Spotlight] Learning-Augmented Frequent Directions

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

In International Conference on Learning Representations, 2025

[ICML] Improved Approximations for Hard Graph Problems using Predictions

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

In International Conference on Machine Learning, 2025

Graph Algorithms

[SIGMOD] Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push

Hao WU, Junhao Gan, Zhewei Wei, Rui Zhang 

In International Conference on Management of Data, 2021

More information about publications can be found on my CV, Google Scholar and DBLP.