Publication

Foundational AI/Machine Learning Research (my student as the first author)

[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. Auto-Prompting SAM for Mobile Friendly 3D Medical Image 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., Qiang, Y, Sultan, R, Bagher-Ebadian, P, Khanduri, V, Chetty, IJ, and Zhu, D. FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images. In the Proceedings of 26th International Conference on Medical Image Computing and Computer Assisted Intervention, Vancouver, Canada. Accept rate: 740/2,250 = 32%.
[AdvML@ICML-23] Khanduri, P, Li, C, Sultan, R, Qiang, Y and Zhu, D. (2023) Proximal Composite Optimization for Distributionally Robust Learning. In the proceedings of new frontiers in adversarial machine learning (AdvML) workshop at ICML, 2023.
[IJCAI-23] Li, X, Pan, D, Li, C, Qiang, Y, and Zhu, D. Negative Flux Aggregation to Estimate Feature Attributions. In the Proceedings of 32st International Joint Conference on Artificial Intelligence, Marco, China. Acceptance rate: 685/4,566 = 15%.
[AAAI-23] Li, X., Li, X., Pan, D, Qiang, Y., and Zhu, D. (2023) Learning Compact Features via In-Training Representation Alignment. In the Proceedings of Thirty-Seventh AAAI Conference on Artificial Intelligence. Accept rate: 1,721/8,777=19.6%.
[NuerIPS-22] Qiang, Y, Pan, D, Li, C, Li, X, Jang, R, and Zhu, D. (2022) AttCAT: Explaining Transformers via Attentive Class Activation Tokens. In the Proceedings of Thirty-sixth Conference on Neural Information Processing Systems. Acceptance rate: 2,665/10,411 = 25.6%.
[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 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).
[IJCAI-21] Pan, D, Li, X and Zhu, D. (2021) Explaining Deep Neural Network Models with Adversarial Gradient Integration. Proceedings of 30th International Joint Conference on Artificial Intelligence, Montreal, Canada. Acceptance rate: 587/4,204=13.9%.
[DMKD] 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.
[AAAI-21] Li, X, Li, X, Pan,D and Zhu, D. (2021) Improving adversarial robustness via probabilistically compact loss with logit constraints. Proceedings of Thirty-Five AAAI Conference on Artificial Intelligence, virtual conference. Code Acceptance rate: 1,692/7,911=21.4%
[IJCAI-20] Pan, D, Li, X, Li, X and Zhu, D. (2020) Explainable recommendation via interpretable feature mapping and evaluating explainability. Proceedings of 29th International Joint Conference on Artificial Intelligence, Yokohama, Japan. Acceptance rate: 592/4,717=12.6%
[IJCNN-20] Qiang, Y, Li, X and Zhu, D. (2020) Toward tag-free aspect based sentiment analysis: a multiple attention network approach. Proceedings of International Joint Conference on Neural Networks, Glasgow, Scotland, UK.
[AAAI-20] Li, X, Li, X, Pan,D and Zhu, D. (2020) On the learning behavior of logistic and softmax losses for deep neural networks. In the proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, USA. Acceptance rate: 1,591/7,737=20.6%.
[PRL] Li, X and Zhu, D. (2018) Robust feature selection via l 2, 1 -norm in finite mixture of regression. Pattern Recognition Letters, 108 (2018) 15–22.
[SAM] Wang, L, Zhu, D. and Dong, M (2018) Clustering over-dispersed data with mixed feature types. Statistical Analysis and Data mining, 2018;11:55–65.
[PRL] Li, X, Zhu, D. and Dong, M (2018) Multinomial classification with class-conditional overlapping sparse feature groups. Pattern Recognition Letters, vol 101, Jan. 2018, pp 37-43.
[ICDM-17] Wang, L, Li, Y, Zhou, J, Zhu, D and Ye, J (2017) Multi-task Survival Analysis. Proceedings of 2017 IEEE International Conference on Data Mining. Acceptance rate: 72/778=9.25%.

Use-Inspired AI Research (my student as the first author)

[Transportation] Kabir, R., Remias, S, Wadell, J, and Zhu, D. (2023) Time-Series Fuel Consumption Prediction Assessing Delay Impacts on Energy using Vehicular Trajectory. To appear in Transportation Research Part D.
[Medical Imaging] Li, X, Bagher, HE, Kim, J, Zhu, D, and Chetty, I. (2022) An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning. Medical Physics, https://doi.org/10.1002/mp.15982.
[Medical Imaging] 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.
[Medical Imaging] Li, X, Li, C., and Zhu, D. (2020) COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray. In the proceedings of 2020 International Conference on Bioinformatics and Biomedicine (BIBM-20). Code BibTeX
[Medical Imaging] Li, X., Cao, R., & Zhu, D. (2020). Vispi: Automatic visual perception and interpretation of chest X-rays. In the proceedings of the Medical Imaging with Deep Learning (MIDL-20) Conference, Montreal, CA. Code BibTeX
[EHR] 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, Houston, USA. (Best Student Paper Award, *Corresponding Autor) PubMed 32477657
[Medical Imaging] Li, X. and Zhu, D. (2020). Robust detection of adversarial attacks on medical images. IEEE International Symposium on Biomedical Imaging (ISBI-20), Iowa City, USA. Code DBLP
[Medical Imaging] 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, USA. Code DBLP
[EHR] Wang, Dong, M, Towner, E and Zhu, D. (2019) Prioritization of multi-level risk factors for obesity. In the proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM-19), 1065-1072. GoogleScholar DBLP
[EHR] Nezhad, MZ, Sadati, N, Yang, K and Zhu, D. (2019) A deep active survival analysis approach for precision treatment recommendations: application of prostate cancer. Expert Systems with Applications. Vol. 15, 16-26. GoogleScholar BibTeX
[EHR] Wang, L, Zhu, D., Towner, E and Dong, M (2018) Obesity risk factors ranking using multi-task learning. IEEE Conference on Biomedical and Health Informatics (IEEE-BHI 2018), Las Vegas, March, 2018. GoogleScholar BibTeX
[EHR] Li, X, Zhu, D and Levy, P (2017) Predictive Deep Network with Leveraging Clinical Measure as Auxiliary Task. In the proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM’17) GoogleScholar BibTeX
[EHR] Li, X, Zhu, D, Dong, M, Nezhad, MZ and Levy, P (2017) SDT: A Tree Method for Detecting Patient Subgroups with Personalized Risk Factors. In the proceedings of 2017 American Medical Informatics Association (AMIA) Summit on Clinical Research Informatics, San Francisco, March 2017. GoogleScholar BibTeX
[EHR] Nezhad, MZ, Zhu, D, Li, X, Yang, C and Levy, P (2016) SAFS: A Deep Feature Selection Approach for Precision Medicine. In the proceedings of 2016 IEEE Inernational Conference on Bioinformatics and Biomedicine (IEEE BIBM 2016).

Bioinformatics & Computational Biology

Uprety, D, Zhu, D, and West, H. ChatGPT- a promising generative AI tool and its implications on cancer care. Cancer, https://doi.org/10.1002/cncr.34827.
Hicks, S.D., Zhu, D., Sullivan, R., Kannikeswaran, N., Meert, K., Chen, W., Suresh, S. and Sethuraman, U., 2023. Saliva microRNA Profile in Children with and without Severe SARS-CoV-2 Infection. International Journal of Molecular Sciences, 24(9), p.8175.
Li, C, Sullivan, R, Zhu, D, and Hick, S (2022) Putting the ‘mi’ in omics: discovering miRNA biomarkers for pediatric precision care. Pediatrics Research, https://doi.org/10.1038/s41390-022-02206-5.
Wang, L, Acharya, L, Bai, C and Zhu, D (2017) Transcriptome assembly strategies for precision medicine. Quantitative Biology, pp 1-11, https://doi.org/10.1007/s40484-017-0109-2.
Hou, J., Acharya, L., Zhu, D. and Chen, J. (2016) An overview of bioinformatics methods for modeling biological pathways in yeast. Briefings in Functional Genomics, 15(2), 95-108
Zhu, D, Deng, N, and Bai C. (2014) An event-based computational framework for comparing transcriptomes. IEEE Transaction on NanoBioScience, DOI: 10.1109/TNB.2015.2388593.
Deng, N, Sanchez, C, Lasky, J, Zhu, D. (2013) Detecting splicing variants from non-differentially expressed genes of human idiopathic pulmoary fibrosis. PLoS One 8(7):e68352. doi:10.137/journal.pone.0068352.
Judeh, T, Johnson, C, Kumar, A, Zhu, D (2013) TEAK: Topological Enrichment Analysis frameworK for detecting activated biological subpathways. Nucleic Acids Res., doi: 10.1093/nar/gks1299.
Deng, N and Zhu, D. (2013). Detecting various types of differential splicing events using RNA-Seq data. Proceedings of 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB'13).
Acharya, L, Judeh, T, Wang, G, Zhu, D. (2012) Optimal structural inference of signaling pathways from overlapping and unordered gene sets. Bioinformatics, doi 10.1093/bioinformatics/btr696, 28(4), 546-556
Acharya, L, Judeh, T, Duan, Z, Rabbat, M, Zhu, D. (2011) GSGS: A computational framework for reconstructing signaling pathways from gene sets. IEEE/ACM transaction on Computational Biology and Bioinformatics (TCBB), 9(2), 438-450.
Deng N, Puetter, A, Zhang, K, Johnson, K., Zhao, Z, Taylor, C, Flemington, E and Zhu, D. (2011) Isoform-level microRNA-155 Target Prediction using RNA-seq. Nucleic Acids Res., doi: 10.1093/nar/gkr042.
Xu G, Deng N, Zhao, Z, Flemington EK, Zhu D. (2011) SAMMate: A GUI tool for processing short read alignment information in SAM/BAM format. Source Code for Biology and Medicine, 6:2. Software
Zhu, D, Acharya, L, Zhang, H. (2011) A generalized multivariate approach to pattern discovery from replicated and incomplete genome-wide measurements. IEEE/ACM transaction on Computational Biology and Bioinformatics (TCBB), 8(5), pp1153-1169.

Selected Recent Publications (entire period)

Pre-Prints (my student as the first author)

Foundational AI/Machine Learning Research (my student as the first author)

Use-Inspired AI Research (my student as the first author)

Bioinformatics & Computational Biology

Selected Federal Funding

Software