Jialu Wang

faldict [at] ucsc [dot] edu

Hi! My name is Jialu Wang. Currently, I am a fifth-year PhD candidate at UC Santa Cruz, CS Department. I work with Prof. Yang Liu and Prof. Xin Wang. My research interest is algorithmic fairness in decision making and AI models. More specifically, I study how to train fair classifiers with imperfect information and understand the impact of the implicit biases sourced from the training data, with the applications on vision and language learning.

Prior to that, I received my Bachelor degree of Computer Science at Shanghai Jiao Tong University in 2019.

I am on the job market now!


I enjoy road trips. I have plotted the road trip map, highlighting the US states and national parks I have visited.


[1] Zhaowe Zhu*, Jialu Wang*, Hao Cheng, and Yang Liu. Unmasking and improving data credibility: A study with datasets for training harmless language models. In The Twelfth International Conference on Learning Representations, 2024.
[2] Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, and Kun Zhang. Procedural fairness through decoupling objectionable data generating components. In The Twelfth International Conference on Learning Representations, 2024.
[3] Zhen Zhang, Jialu Wang, and Xin Eric Wang. Parameter-efficient cross-lingual transfer of vision and language models via translation-based alignment. In Findings of EMNLP 2023.
[4] Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, and Xin Eric Wang. T2IAT: Measuring valence and stereotypical biases in text-to-image generation. In Findings of ACL 2023.
[5] Yatong Chen, Reilly Raab, Jialu Wang and Yang Liu. Fairness transferability subject to bounded distribution shift. In NeurIPS 2022. Best paper award ICML 2022 Workshop on Adversarial Machine Learning Frontiers
[6] Jialu Wang, Xin Eric Wang and Yang Liu. Understanding Instance-Level Impact of Fairness Constraints In ICML 2022.
[7] Zhaowei Zhu, Jialu Wang and Yang Liu Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features. In ICML 2022.
[8] Jialu Wang, Yang Liu and Xin Eric Wang. Assessing Multilingual Fairness in Pre-trained Multimodal Representations In Findings of ACL 2022.
[9] Yang Liu and Jialu Wang. Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial In NeurIPS 2021.
[10] Jialu Wang, Yang Liu and Xin Eric Wang. Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search In EMNLP 2021.
[11] Jialu Wang, Yang Liu and Caleb Levy. Fair Classification with Group-Dependent Label Noise In FAccT (2021).
[12] Yatong Chen, Jialu Wang and Yang Liu. Learning to incentivize improvements from strategic agents. In TMLR. Best paper award at ICML 2021 workshop on Algorithmic Recourse.
[13] Ruijie Wang, Yuchen Yan, Jialu Wang, Yuting Jia, Ye Zhang, Weinan Zhang and Xinbing Wang. “AceKG: A Large-scale Knowledge Graph for Academic Data Mining.” CIKM (2018).
[14] Yining Hong, Jialu Wang, Yuting Jia, Weinan Zhang, Xinbing Wang. Academic Reader: An Interactive Question Answering System on Academic Literatures. In AAAI 2019 Demonstration Program.
[15] Chen Wang, Xiangyu Chen, Zelin Ye, Jialu Wang, Ziruo Cai, Shixiang Gu, and Cewu Lu. Trl: Discriminative hints for scalable reverse curriculum learning. IROS 2019.