I am a Ph.D. student in ECE at 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 trustworthy machine learning with a special focus on comprehending and enhancing the effectiveness of trustworthy machine learning methods in adverse scenarios. Some topics I explored recently include supervision-efficient fair machine learning and fair machine learning security.
Educations
2021-26 (Expected) Purdue University Ph.D. in Electrical and Computer Engineering
2019-21 University of Michigan M.S. in Applied Statistics
2013-17 Xiamen University B.S. in Statistics
Publications
2024
NeurIPS'24 FIARSE: Model-Heterogeneity Federated Learning via Importance-Aware Submodel Extraction. Feijie Wu, Xingchen Wang, Yaqing Wang, Tianci Liu, Lu Su, Jing Gao.
NeurIPS'24 Counterfactual Fairness by Combining Factual and Counterfactual Predictions. Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David Inouye.
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, Monica Cheng, Tuo Zhao, Jing Gao.
(Oral, 2.8%)
SenSys'24 mmCLIP: Boosting mmWave-based Zero-shot HAR via Signal-Text Alignment. Qiming Cao, Hongfei Xue, Tianci Liu, Xingchen Wang, Haoyu Wang, Xincheng Zhang, Lu Su.
SenSys'24 Towards Efficient Heterogeneous Multi-Modal Federated Learning with Hierarchical Knowledge Disentanglement. Xingchen Wang, Haoyu Wang, Feijie Wu, Tianci Liu, Qiming Cao, Lu Su.
ICML'24 LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models. Tianci Liu, Haoyu Wang, Shiyang Wang, Yu Cheng, Jing Gao
(Spotlight, 3.8%)
ICLR'24 Towards Poisoning Fair Representations. Tianci Liu, Haoyu Wang, Feijie Wu, Hengtong Zhang, Pan Li, Lu Su, Jing Gao.
2023
EMNLP'23 Findings HadSkip: Homotopic and Adaptive Layer Skipping of Pre-trained Language Models for Efficient Inference. Haoyu Wang, Yaqing Wang, Tianci Liu, Tuo Zhao, Jing Gao.
ICML'23 Optimization for Amortized Inverse Problems. Tianci Liu*, Tong Yang*, Quan Zhang, Qi Lei (Equal Contribution).
AAAI'23 SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification. Tianci Liu, Haoyu Wang, Yaqing Wang, Xiaoqian Wang, Lu Su, Jing Gao.
(Distinguished Paper Award, 12 out of 8777 submissions)
Stat Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks. Daiwei Zhang, Tianci Liu, Jian Kang.
Scientific Reports Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals. Laura Zichi, Tianci Liu, Elizabeth Drueke, Liuyan Zhao, and Gongjun Xu.
2022
Frontiers in Psychology Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder. Tianci Liu, Chun Wang, Gongjun Xu.