Yuhan Liu

刘宇涵 Rice University, University of Hong Kong yuhan-liu at rice dot edu yuhanl at hku dot hk

About me

I am a postdoc at Rice University hosted by Prof. Maryam Aliakbarpour, Prof. Vladimir Braverman, and Prof. Nai-Hui Chia. I will join the University of Hong Kong as tenure-track Assistant Professor in December 2025. I obtained my Ph.D. in ECE at Cornell University in 2024, advised by Prof Jayadev Acharya.

I graduated from the department of automation at Tsinghua University in 2018 with honor, advised by Prof. Yipeng Li. I also worked with Prof. Liwei Wang from Peking University. In addition, I worked as research intern in Google, Microsoft Research Asia, and Megvii (Face++).

My research interest lies broadly in theoretical computer science. Topics I have worked on include differential privacy, statistical estimation, robust statistics, and more recently quantum computing.

Prospective students: I am actively looking for Ph.D. students to join in Fall 2027, with at least one student in the area of quantum computing. Please email me your CV, publications, and a brief research statement.

Preprints

Author names are in alphabetical order unless otherwise indicated.

• High-Dimensional Robust Mean Estimation with Untrusted Batches
  Maryam Aliakbarpour, Vladimir Braverman, Yuhan Liu, Junze Yin
  arXiv:2602.20698[cs.LG]

• Shadow Tomography Against Adversaries
  Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Chia-Ying Lin, Yuhan Liu, Aadil Oufkir, Yu-Ching Shen
  arXiv:2512.05451[quant-ph]

• Pauli measurements are near-optimal for single-qubit tomography
  Jayadev Acharya, Abhilash Dharmavarapu, Yuhan Liu, Nengkun Yu
  Accepted as contributed talk at QIP 2026 as Pauli Tomography at Your Fingertips
  arXiv:2507.22001[quant-ph]

• Quantum state testing with restricted measurements
  Yuhan Liu, Jayadev Acharya
  arXiv:2408.17439[quant-ph]

Publication

Author names are in alphabetical order unless otherwise indicated.

• Adversarially robust quantum state learning and testing
  Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Yuhan Liu
  The 66th Annual Symposium on Foundations of Computer Science (FOCS 2025)
  Accepted as contributed talk at QIP 2026
  NSF storyRice News

• Pauli measurements are not optimal for single-copy tomography
  Jayadev Acharya, Abhilash Dharmavarapu, Yuhan Liu, Nengkun Yu
  The 57th Annual ACM Symposium on Theory of Computing (STOC 2025)
  Accepted as contributed talk at QIP 2026 as Pauli Tomography at Your Fingertips

• The role of randomness in quantum state certification with unentangled measurements
  Yuhan Liu, Jayadev Acharya
  The 34th Annual Conference on Learning Theory (COLT 2024)

• Discrete Distribution Estimation under User-level Local Differential Privacy
  Jayadev Acharya, Yuhan Liu, Ziteng Sun
  The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)

• Algorithms for bounding user contribution for histogram estimation under user-level privacy
  Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
  The 40th International Conference on Machine learning (ICML 2023)
  Short version at ICML 2022 Workshop on Theory and Practice of Differential Privacy

• A perspective on data sharing in digital food safty systems
  Chenhao Qian, Yuhan Liu, Cecil Barnett-Neefs, Sudeep Salgia, Omer Serbetci, Aaron Adalja, Jayadev Acharya, Qing Zhao, Renatta Ivanek, Martin Wiedmann
  Critical Reviews in Food Science and Nutrition, DOI: 10.1080/10408398.2022.2103086

• Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
  Jayadev Acharya, Clement Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi
  35th Conference on Neural Inforamtion Processing Systems (NeurIPS 2021)

• Estimating sparse discrete distributions under privacy and communication constraints
  Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun
  The 32nd International Conference on Algorithmic Learning Theory (ALT 2021).

• Learning discrete distributions: user vs item level privacy
  Yuhan Liu, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, Michael Riley
  34th Conference on Neural Information Processing Systems (NeurIPS 2020)

• Interactive inference under information constraints
  Jayadev Acharya, Clement Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi
  ISIT 2021. Accepted by IEEE Transactions on Information Theory

Work Experience

• May 2022-Dec 2022 Google | Research Intern

• May 2021-Dec 2021 Google | Research Intern

• Nov 2017-Jun 2018 Microsoft Research Asia | Research Intern

• Sep 2017-Oct 2017 Megvii (Face++) | Research Intern

Teaching

Teaching Assistant:

• ECE4200 Fundamentals of Machine Learning, Spring and Fall 2020

Academic Service

Reviewer: NeurIPS 2021, ICLR 2022, ICML 2022, NeurIPS 2022, ICLR 2023, AISTATS 2023, NeurIPS 2023

Honors

Fellowships and Scholarships

• Academic Excellence Scholarship, Tsinghua University, Oct 2015 and Oct 2016

• Cornell Fellowship, Sep 2018

Awards

• Excellent Graduating Student of Tsinghua University, Jul 2018

Education

• 2014-2018, Tsinghua University, B.Eng. in Automation (with honor)
  Thesis: Monocular Semantic SLAM with Object Augmented Pose-Graph Map

• 2018-present, Cornell University, Ph.D. in Electrical and Computer Engineering