Yuguang Yao

I am a Senior Research Scientist at Intuit AI Research (Dec 2024 – present). I received my Ph.D. in Computer Science and Engineering from Michigan State University, advised by Prof. Sijia Liu. Before that, I obtained my B.S. in Automation from Tsinghua University.

My research focuses on LLM Agents, Adversarial Machine Learning, Model Pruning, Prompt Learning, and Optimization (black-box, zeroth-order, bi-level).

Email: tony.yuguang.yao@gmail.com  |  CV  |  Google Scholar  |  GitHub  |  LinkedIn

Education

  • Ph.D. in CSE, Michigan State University, Jan 2021 – May 2025. Advisor: Sijia Liu
  • Ph.D. Student in CSE, Tsinghua / MSU, Aug 2018 – Dec 2020. Advisors: Zhichao Cao, Yunhao Liu
  • B.S. in Automation, Tsinghua University, Aug 2014 – Jul 2018. Advisor: Hong Wang
  • Exchange, École Polytechnique Fédérale de Lausanne (EPFL), Aug 2016 – Feb 2017

Experience

  • Senior Research Scientist, Intuit AI Research, Dec 2024 – Present
  • Research Intern, Cisco Research, Feb 2023 – Jun 2024. Advisor: Gaowen Liu
  • Research Intern, MIT-IBM Watson AI Lab, May 2021 – Aug 2021. Advisor: Quanfu Fan
  • Research Intern, DiDi AI Lab, Nov 2017 – Feb 2018. Advisor: Yashu Liu
  • Research Intern, HKUST, Jun 2017 – Sep 2017. Advisor: Pan Hui

Selected Publications

  1. Y. Yao*, Y. Chen*, et al., Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning, ICLR 2026.
  2. K. Chen, Z. Lin, …, Y. Yao, et al., R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation, ACL 2025.
  3. Y. Yao*, J. Liu*, et al., Can Adversarial Examples Be Parsed to Reveal Victim Model Information?, WACV 2025.
  4. Y. Yao*, Z. Pan*, et al., From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models, NeurIPS 2024.
  5. Y. Yao, G. Xiao, et al., Reverse Engineering of Deceptions on Machine- and Human-Centric Attacks, Foundations and Trends in Privacy and Security 2024.
  6. S. Pal, Y. Yao, et al., Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency, ICLR 2024.
  7. J. Jia*, J. Liu*, …, Y. Yao, et al., Model Sparsity Can Simplify Machine Unlearning, NeurIPS 2023 Spotlight.
  8. A. Chen, Y. Yao, et al., Understanding and Improving Visual Prompting: A Label-Mapping Perspective, CVPR 2023.
  9. Y. Yao*, Y. Zhang*, et al., Advancing Model Pruning via Bi-level Optimization, NeurIPS 2022.
  10. Y. Yao*, Y. Gong*, et al., Reverse Engineering of Imperceptible Adversarial Image Perturbations, ICLR 2022.
  11. Y. Zhang, Y. Yao, et al., How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective, ICLR 2022 Spotlight.

Service & Awards

  • Workshop Chair: AdvML Frontiers at ICML’22, ICML’23, NeurIPS’24
  • Reviewer: NeurIPS, ICLR, ICML, ACL, CVPR, ACMMM, ICASSP, TPAMI
  • Travel Grants: NeurIPS 2022, CVPR 2023, NeurIPS 2024
  • Best Poster Award, EWSN 2019
  • Cisco Research Award ($75K) for “Towards Lifelong LMM Agents in Embodied AI”