I am a Postdoctoral Associate at the Singapore-MIT Alliance for Research and Technology (SMART), a major research enterprise established by the Massachusetts Institute of Technology (MIT) in partnership with the National Research Foundation of Singapore (NRF). I am currently working with Prof. Daniela Rus and Assoc. Prof. Bryan Kian Hsiang Low.

I received my Ph.D. in Data Science from the National University of Singapore (NUS), supervised by Assoc. Prof. Bryan Kian Hsiang Low in 2024. I received my Bachelor of Science (Honors) in Data Science & Analytics and a minor in Computer Science from NUS in 2020. My Ph.D. was supported by the President’s Graduate Fellowship jointly offered by the NUS Graduate School Integrative Sciences and Engineering Programme (ISEP) and the Institute of Data Science (IDS), and the Singapore Data Science Consortium (SDSC) Dissertation Research Fellowship.

Research Interests

My interests include, but not limited to, the following areas:

• Data-centric AI (e.g., data valuation & selection, collaborative machine learning, incentives, fairness)
• Resource-efficient machine learning (e.g., Bayesian optimization)
• Large language models (e.g., inference-time techniques, prompting)
• Deep learning & applications

Publications

* = equal contribution

2025

• TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding.
  Zhaoxuan Wu*, Zijian Zhou*, Arun Verma, Alok Prakash, Daniela Rus, and Bryan Kian Hsiang Low.
  In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL-25).
  [code]

• Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions.
  Rui Qiao, Zhaoxuan Wu, Jingtan Wang, Pang Wei Koh, and Bryan Kian Hsiang Low.
  In Proceedings of the 13th International Conference on Learning Representations (ICLR-25).
  [code]

• Paid with Models: Optimal Contract Design for Collaborative Machine Learning.
  Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, and Bryan Kian Hsiang Low.
  In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI-25) - Oral.
  [code]

2024

• Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars.
  Zhaoxuan Wu*, Xiaoqiang Lin*, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, and Bryan Kian Hsiang Low.
  In Advances in Neural Information Processing Systems 37: 38th Annual Conference on Neural Information Processing Systems (NeurIPS-24).
  Also in ICML Workshop on In-Context Learning 2024.
  [code]

• Localized Zeroth-Order Prompt Optimization.
  Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, and Bryan Kian Hsiang Low.
  In Advances in Neural Information Processing Systems 37: 38th Annual Conference on Neural Information Processing Systems (NeurIPS-24) - Spotlight.
  Also in ICML Workshop on In-Context Learning 2024.
  [code]

• Position Paper: Data-Centric AI in the Age of Large Language Models.
  Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, and Bryan Kian Hsiang Low.
  In Proceedings of the Conference on Empirical Methods in Natural Language Processing 2024 (EMNLP-24) Findings.

• Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers.
  Xiaoqiang Lin*, Zhaoxuan Wu*, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, and Bryan Kian Hsiang Low.
  In Proceedings of the 41st International Conference on Machine Learning (ICML-24).
  Also in NeurIPS Workshop on Instruction Tuning and Instruction Following 2023.
  [project page | code]

• Distributionally Robust Data Valuation.
  Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, and Bryan Kian Hsiang Low.
  In Proceedings of the 41st International Conference on Machine Learning (ICML-24).
  [code]

• Incentive-Aware Federated Learning with Training-Time Model Rewards.
  Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar, and Bryan Kian Hsiang Low.
  In Proceedings of the 12th International Conference on Learning Representations (ICLR-24).
  [code]

2023

• FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery.
  Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, and Bryan Kian Hsiang Low.
  In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS-23).
  [code]

2022

• DAVINZ: Data Valuation using Deep Neural Networks at Initialization.
  Zhaoxuan Wu, Yao Shu, and Bryan Kian Hsiang Low.
  In Proceedings of the 39th International Conference on Machine Learning (ICML-22).
  [code]

• Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search.
  Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, and Bryan Kian Hsiang Low.
  In Advances in Neural Information Processing Systems 35: 36th Annual Conference on Neural Information Processing Systems (NeurIPS-22).
  [code]

2021

• Validation Free and Replication Robust Volume-based Data Valuation.
  Xinyi Xu*, Zhaoxuan Wu*, Chuan Sheng Foo, and Bryan Kian Hsiang Low.
  In Advances in Neural Information Processing Systems 34: 35th Annual Conference on Neural Information Processing Systems (NeurIPS-21).
  [code]

• Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization.
  Quoc Phong Nguyen*, Zhaoxuan Wu*, Bryan Kian Hsiang Low, and Patrick Jaillet.
  In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI-21).
  [code]

Book Chapters

• Data Valuation in Federated Learning.
  Zhaoxuan Wu, Xinyi Xu, Rachael Hwee Ling Sim, Yao Shu, Xiaoqiang Lin, Lucas Agussurja, Zhongxiang Dai, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang, and Bryan Kian Hsiang Low.
  In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 15, pages 281-296, Academic Press, 2024.

• Fairness in Federated Learning.
  Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, Rachael Hwee Ling Sim, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang, and Bryan Kian Hsiang Low.
  In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 8, pages 143-160, Academic Press, 2024.

• Incentives in Federated Learning.
  Rachael Hwee Ling Sim, Sebastian Shenghong Tay, Xinyi Xu, Yehong Zhang, Zhaoxuan Wu, Xiaoqiang Lin, See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang, and Bryan Kian Hsiang Low.
  In L. M. Nguyen, T. N. Hoang, P.-Y. Chen, editors, Federated Learning: Theory and Practice, chapter 16, pages 299-309, Academic Press, 2024.

Invited Talks

• Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers.
  @ Deep Learning and Optimization Seminar (Jointly organized by Westlake University, CityU, Peking University). Oct 24, 2023.

Academic Activities

• Conference Reviewer
  • AAAI 2024, 2025
  • AISTATS 2024
  • IJCAI 2024
  • ICLR 2023, 2024, 2025
  • AAMAS 2023, 2024, 2025
  • ICML 2022, 2023, 2024, 2025
  • NeurIPS 2022, 2023, 2024
  • ACML 2022, 2023
• Received Top Reviewer for NeurIPS 2023
• Received Notable Reviewer for ICLR 2025