Publications

[ECAI-25] Zade, S.Z., Qiang, Y., Zhou, X., Zhu, H., Roshani, M.A., Khanduri, P. and Zhu, D., 2025. Automatic Calibration for Membership Inference Attack on Large Language Models. 

[ECAI-25] Sultan, R, Li, C, Zhu, H, Khanduri, P, Brocanelli, M, Zhu, D. GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure.

[JMIR-AI-25] Roshani, M.A., Zhou, X., Qiang, Y., Suresh, S., Hicks, S., Sethuraman, U. and Zhu, D., 2025. Generative Large Language Model—Powered Conversational AI App for Personalized Risk Assessment: Case Study in COVID-19. JMIR AI, 4(1), e67363.

[IJCNN-25] Qiang, Y, Li, C, Khanduri, P, and Zhu, D. Interpretability-Aware Vision Transformer. 

[WACV-25] Li, C, Zhu, H, Sultan, R, Khanduri, P, Qiang, Y, Chetty, I, Thind, K, and Zhu, D. MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training. 

[WACV-25] Li, C, Khanduri, P, Qiang, Y, Sultan, R, Chetty, I and Zhu, D. AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation. 

[ECCV-24] Qiang, Y, Li, C, Khanduri, P, and Zhu, D. Fairness-aware Vision Transformer via Debiased Self-Attention. In the proceedings of the 2024 European Conference on Computer Vision (ECCV-24). Accept rate is 2,395/8,585 = 27.9%. 

[TheWebConf-24] Zamiri, M, Qiang, Y, Nikolaev, F, Zhu, D, Kotov, A. 2024. Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge Graph. In the proceedings of the 2024 ACM Web Conference. Accept rate is 806/4,028 = 20.2%. 

[CHI-24] Zheng, W., Walquist, E., Datey, I., Zhou, X., Berishaj, K., Mcdonald, M., Parkhill, M., Zhu, D., & Zytko, D. 2024. It's not what we were trying to get at, but I think maybe it should be”: Learning how to do trauma-informed design with a data donation platform for online dating sexual violence. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11–16, 2024, Honolulu, HI, USA (pp. 1-15). ACM. https://doi.org/10.1145/3613904.3642045. Accept rate is 1,060/4,028 = 26.3%.

[AAAI-24] Zhu, Z., Chen, H., Zhang, J., Wang, X., Jin, Z., Xue, M., Zhu, D. and Choo, K.K.R., 2023. MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks. In the Proceedings of Thirty-Seventh AAAI Conference on Artificial Intelligence. Accept rate: 2,342/12,100 = 23.75%.

[MICCAI-23] Li, C., Bagher-Ebadian, H., Goddla, V., Chetty, I. J., Zhu, D. (2023) FocalUNETR: A Focal Transformer for Boundary-aware Prostate Segmentation using CT Images. Accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI-23). Acceptance rate: 740/2250 = 32%.

[ECML-22] Li, C., Dong, Z, Fisher, N, and Zhu, D. (2022) Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation. To appear in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Acceptance rate: 242/932 = 26%.

[AdvML@ICML-22] Li, X., Qiang, Y. Li, C., Liu, S. and Zhu, D. (2022) Saliency guided adversarial training for tackling generalization gap with applications to medical imaging classification system. In the proceedings of new frontiers in adversarial machine learning (AdvML) workshop at ICML, 2022. 

[IJCAI-22] Qiang, Y, Li, C, Brocanelli, M, Zhu, D. (2022) Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN. Proceedings of 31st International Joint Conference on Artificial Intelligence, Messe Wien, Vienna, Austria. Acceptance rate: 681/4,535 = 15%.

[IJCNN-22] Qiang, Y, Supriya TS. Kumar, Brocanelli, M, Zhu, D. (2022) Tiny RNN Model with Certified Robustness for Text Classification. Proceedings of International Joint Conference on Neural Networks (Oral Presentation). 

Pan, D, Li, X and Zhu, D. (2021) Explaining Deep Neural Network Models with Adversarial Gradient Integration. In the proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Montreal, Canada.

Wang, L. and Zhu, D. (2021). Tackling multiple ordinal regression problems: sparse and deep multi-task learning approaches. Data Mining and Knowledge Discovery (DMKD), 23 March 2021.

Li, X., Pan, D. and Zhu, D. (2021) Defending against adversarial attacks on medical imaging AI system, classification or detection? In the proceedings of IEEE International Symposium on Biomedical Imaging (ISBI-21), virtual conference.

Li, X, Li, X, Pan,D and Zhu, D. (2021) Improving adversarial robustness via probabilistically compact loss with logit constraints. In the proceedings of Thirty-Five AAAI Conference on Artificial Intelligence (AAAI-21), virtual conference. Code

Pan, D, Li, X, Li, X and Zhu, D (2020) Explainable recommendation via interpretable feature mapping and evaluating explainability. to appear in the proceedings of 29th International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.  

Li, X and Zhu, D (2020) COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray. arXiv:3119280 [cs.CV] 

Li, X, Pan,D,Li, X and Zhu, D (2020) Regularize SGD training via aligning min-batches.arXiv:2002.09917 [cs.LG].

Qiang, Y, Li, X and Zhu, D (2020) Toward tag-free aspect based sentiment analysis: a multiple attention network approach. to appear in the proceedings of  International Joint Conference on Neural Networks (IJCNN-20), Glasgow, Scotland, UK.

Li, X, Li, X, Pan,D and Zhu, D (2020) On the learning behavior of logistic and softmax losses for deep neural networks. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York.

Li, X, Zhu, D and Levy, P (2020) Predicting clinical outcomes with patient stratification via deep mixture neural networks. American Medical Informatics Association (AMIA-20) Summit on Clinical Research Informatics, San Francisco, accepted.

Li, X. and Zhu, D. (2020). Robust detection of adversarial attacks on medical images. IEEE International Symposium on Biomedical Imaging (ISBI-20), Iowa City.

Li, X., Hect, J., Thompson, J. and Zhu, D. (2020). Interpreting age effects of human fetal brain from spontaneous fMRI using deep 3D convolutional neural networks. IEEE International Symposium on Biomedical Imaging (ISBI-20), Iowa City.