Xiaoqian Wang (王小倩)
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Assistant Professor 516 Northwestern Ave Email: joywang@purdue.edu |
About Me
My research interests are generally trustworthy machine learning. I am particularly interested in designing novel machine learning models to improve explainability, fairness, and robustness. I also work on the intersection of machine learning and bioinformatics, healthcare. I'm a recipient of the 2022 NSF CAREER award and an IEEE senior member.
I obtained my Ph.D. degree from the University of Pittsburgh in 2019, and my advisor is Prof. Heng Huang. Prior to this, I received my bachelor degree from Zhejiang University in 2013.
My CV can be found here.
I am looking for highly self-motivated Ph.D. students to work on emerging challenges in Machine/Deep Learning and/or Healthcare. Please send me your CV and transcripts if you are interested.
Students
Taeuk Jang (PhD, 2024): “Novel Approaches to Mitigate Data Bias and Model Bias
for Fair Machine Learning Pipelines”
Yipei Wang (PhD student)
Junyi Chai (PhD student)
Shenyu Lu (PhD student)
You-Ru Lu (PhD student), co-advising with Prof. Dengfeng Sun
Hoin Jung (PhD student)
Zhaoying Pan (PhD student)
Xiaoze Liu (PhD student), co-advising with Prof. Jing Gao
Selected Publications
Algorithmic Fairness
A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks
Hoin Jung, Taeuk Jang, and Xiaoqian Wang
In the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
Accepted to Appear
Neural Collapse Inspired Debiased Representation Learning for Min-Max Fairness
Shenyu Lu, Junyi Chai, and Xiaoqian Wang
In the Thirtieth SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)
[Code]
Debiasing Attention Mechanism in Transformer without Demographics
Shenyu Lu, Yipei Wang, and Xiaoqian Wang
In the Twelfth International Conference on Learning Representations (ICLR 2024)
[Code]
Group-Aware Threshold Adaptation for Fair Classification
Taeuk Jang, Pengyi Shi, and Xiaoqian Wang
In the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022)
[Code]
Fairness without Demographics through Knowledge Distillation
Junyi Chai, Taeuk Jang, and Xiaoqian Wang
In the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
[Code]
Distributional Robustness
On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective
Yipei Wang, and Xiaoqian Wang
In the Twenty-Seventh International Conference on Artificial Intelligence and Statistics (AISTATS 2024)
[Code]
Difficulty-based Sampling for Debiased Contrastive Representation Learning
Taeuk Jang, and Xiaoqian Wang
In the Fortieth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)
[Code]
Explainable AI
Great Minds Think Alike: The Universal Convergence Trend of Input Salience
Yipei Wang, Jeffrey Mark Siskind, and Xiaoqian Wang
In the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
Accepted to Appear
Benchmarking Deletion Metrics with the Principled Explanations
Yipei Wang, and Xiaoqian Wang
In the Forty-First International Conference on Machine Learning (ICML 2024)
[Code]
Why Not Other Classes?: Towards Class-Contrastive Back-Propagation Explanations
Yipei Wang, and Xiaoqian Wang
In the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
[Code]
Self-Interpretable Model with Transformation Equivariant Interpretation
Yipei Wang, and Xiaoqian Wang
In the Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS 2021)
[Code]
Shapley Explanation Networks
Rui Wang, Xiaoqian Wang, and David Inouye
In the Ninth International Conference on Learning Representations (ICLR 2021)
[Code]
Machine Learning in Healthcare and Biomedical Domain
Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow
Tianchun Li, Chengxiang Wu, Pengyi Shi, and Xiaoqian Wang
In the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024)
[Code]
Learning the Irreversible Progression Trajectory of Alzheimer's Disease
Yipei Wang, Bing He, Shannon Risacher, Andrew Saykin, Jingwen Yan, and Xiaoqian Wang
In the Twenty-First IEEE International Symposium on Biomedical Imaging (ISBI 2024)
[Code]
Unveiling Dynamics in Standard MRI Sequences through Motion Magnification Techniques
Zhaoying Pan, Vidhya Vijayakrishnan Nair, Qiuting Wen, Yunjie Tong, and Xiaoqian Wang
In Organization for Human Brain Mapping (OHBM 2024)
Accepted for a Poster Presentation