Xiaoqian Wang (王小倩)
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Ron and Dotty Garvin Tonjes Early Career Associate Professor of
Electrical and Computer Engineering 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 honored to have received the 2022 NSF CAREER award, 2025 Purdue Engineering Outstanding Faculty Mentor Award, and to be recognized as 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.
I am always 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, 2025): “From Black Boxes to Verified Mechanisms: Building Trustworthy Machine Learning through Interpretability, Robustness, and Generalization”.
You-Ru Lu (PhD, 2025), co-advised with Prof. Dengfeng Sun: “Federated Learning and its Applications in Multi-UAVs System”.
Junyi Chai (PhD student)
Shenyu Lu (PhD student)
Hoin Jung (PhD student)
Zhaoying Pan (PhD student)
Xiaoze Liu (PhD student), co-advising with Prof. Jing Gao
Ritent (Gavin) Zhang (PhD student)
Selected Publications
Explainable AI
Agree to Disagree: Demystifying Homogenenous Deep Ensemble through Output Distributions
Yipei Wang, and Xiaoqian Wang
In the 13th International Conference on Learning Representations (ICLR 2025)
[Code]
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)
[Code]
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]
Distributional Robustness
Think Twice: Test-Time Reasoning for Robust CLIP Zero-Shot Classification
Shenyu Lu, Zhaoying Pan, and Xiaoqian Wang
In the International Conference on Computer Vision (ICCV 2025)
Accepted to Appear.
On the Alignment between Fairness and Accuracy: from the Perspective of Adversarial Robustnes
Junyi Chai, Taeuk Jang, Jing Gao, and Xiaoqian Wang
In the Forty-Second International Conference on Machine Learning (ICML 2025)
[Code]
Identifying and Mitigating Spurious Correlation in Multi-Task Learning
Junyi Chai, Shenyu Lu, and Xiaoqian Wang
In the Forty-Second IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025)
Mitigating Spurious Correlations in Zero-Shot Multimodal Models
Shenyu Lu, Junyi Chai, and Xiaoqian Wang
In the 13th International Conference on Learning Representations (ICLR 2025)
[Code]
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]
Algorithmic Fairness
Target Bias Is All You Need: Zero-Shot Debiasing of Vision-Language Models with Bias Corpus
Taeuk Jang, Hoin Jung, and Xiaoqian Wang
In the International Conference on Computer Vision (ICCV 2025)
Accepted to Appear.
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)
[Code]
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]
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]
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^ (^ co-corresponding authors)
In the Twenty-First IEEE International Symposium on Biomedical Imaging (ISBI 2024)
[Code]
Inverse Problem Antidote (IPA): Modeling of Systems Biology Model with Invertible Neural Networks
Linlin Li*, Shenyu Lu*, David Umulis^, and Xiaoqian Wang^ (* co-first authors, ^ co-corresponding authors)
In the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024 Workshop)
[Code]