Tianci Liu

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.
I am broadly interested in building helpful and reliable AI/ML. My current research focuses on building knowledgeable LLMs in efficiency. To this end, I develop tools to conduct precise and generalizable knowledge update for LLMs. I also work on algorithmic fairness and its safety, inverse problems and its applications, and AI-empowered wireless sensing systems.
Feel free to drop me an email if you are interested in my research or just want to chat. I am always open to new ideas and collaborations.
news
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. |
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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
- ACL’25 FindingsRoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference OptimizationIn Findings of the Association for Computational Linguistics: ACL 2025, 2025
- ICML’25Mitigating Heterogeneous Token Overfitting in LLM Knowledge EditingIn The Fourty-Second International Conference on Machine Learning, 2025
- ICLR’25Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-TuningIn The Thirteenth International Conference on Learning Representations, 2025
- UAI’25Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright ProtectionIn The Fourty-First Conference on Uncertainty in Artificial Intelligence, 2025
- ICML’24LIDAO: Towards Limited Interventions for Debiasing (Large) Language ModelsIn The Fourty-First International Conference on Machine Learning, 2024
- ICLR’24Towards Poisoning Fair RepresentationsIn The Twelfth International Conference on Learning Representations, 2024
- AAAI’23Simfair: A unified framework for fairness-aware multi-label classificationIn Proceedings of the AAAI Conference on Artificial Intelligence, 2023
- ICML’23Optimization for amortized inverse problemsIn The Fortieth International Conference on Machine Learning, 2023
- EMNLP’24RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuningIn Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
- NeurIPS’24Counterfactual Fairness by Combining Factual and Counterfactual PredictionsIn The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024