Tianci Liu

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I am a Ph.D. Candidate in ECE@Purdue University under the supervision of Prof. Jing Gao. Before coming to Purdue, I spent two wonderful years at University of Michigan to acquire my MS degree in Statistics. Prior to that, I got my BS degree from Xiamen University.

My research goal is to develop principled methods for building knowledgeable and efficient machine learning models. My work is primarily focused on the following pillars:

  • Knowledgeable & Efficient LLMs: I design scalable methods for knowledge editing, retrieval-augmented generation (RAG), and efficient fine-tuning to build precise, adaptable, and resource-efficient (M)LLMs, enabling seamless integration of diverse knowledge sources in real-world deployments.

  • Trustworthy AI in a Data-Efficient Manner: I create principled methods to understand and improve fairness and integrity in AI systems with minimal data, mitigating risks and delivering reliable outcomes with minimal data requirement.

  • Applications: I apply AI/ML to real-world challenges across domains, such as sensing systems, education, biomedical imaging, and physics-informed modeling.

I am on the job market and am open to academic positions and industrial research roles. If you believe I might be a good fit for your institution or organization, please feel free to reach out to me liu3351[AT]purdue.edu

news

Aug 20, 2025 Our paper “Towards Universal Debiasing for Language Models-based Tabular Data Generation” and “Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs” were accepted at EMNLP’25 Findings.
May 15, 2025 Our paper “RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization” was accepted at ACL’25 Findings.
May 08, 2025 Our paper “RAM-Hand: Robust Acoustic Multi-Hand Pose Reconstruction Using a Microphone Array” won Best Paper Award at Sensys’25.
May 06, 2025 Our paper “Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection” was accepted at UAI’25.
May 01, 2025 Our paper “Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing” was accepted at ICML’25.

selected publications

  1. ACL’25 Findings
    RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
    Tianci Liu*, Haocheng Jiang*, Tianze Wang, and 5 more authors
    In Findings of the Association for Computational Linguistics: ACL 2025, 2025
  2. ICML’25
    Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing
    Tianci Liu, Ruirui Li, Zihan Dong, and 6 more authors
    In The Fourty-Second International Conference on Machine Learning, 2025
  3. ICLR’25
    Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
    Tianci Liu, Ruirui Li, Yunzhe Qi, and 8 more authors
    In The Thirteenth International Conference on Learning Representations, 2025
  4. UAI’25
    Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection
    Tianci Liu, Tong Yang, Quan Zhang, and 1 more author
    In The Fourty-First Conference on Uncertainty in Artificial Intelligence, 2025
  5. ICML’24
    LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
    Tianci Liu, Haoyu Wang, Shiyang Wang, and 2 more authors
    In The Fourty-First International Conference on Machine Learning, 2024
    Spotlight Paper
  6. ICLR’24
    Towards Poisoning Fair Representations
    Tianci Liu, Haoyu Wang, Feijie Wu, and 4 more authors
    In The Twelfth International Conference on Learning Representations, 2024
  7. AAAI’23
    Simfair: A unified framework for fairness-aware multi-label classification
    Tianci Liu, Haoyu Wang, Yaqing Wang, and 3 more authors
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2023
    Distinguished Paper Award
  8. ICML’23
    Optimization for amortized inverse problems
    Tianci Liu*, Tong Yang*, Quan Zhang, and 1 more author
    In The Fortieth International Conference on Machine Learning, 2023
  9. EMNLP’24
    RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning
    Haoyu Wang, Tianci Liu, Ruirui Li, and 3 more authors
    In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
    Oral Paper
  10. NeurIPS’24
    Counterfactual Fairness by Combining Factual and Counterfactual Predictions
    Zeyu Zhou, Tianci Liu, Ruqi Bai, and 3 more authors
    In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024