[1]    Nezhad, MZ, Sadati, N, Yang, K and Zhu, D. A deep active survival analysis approach for precision treatment recommendations: application of prostate cancer. Expert Systems with Applications. Vol. 15, 16-26.  

[2]    Zheng, J, Gao, L, Zhang, H, Zhu, D, Wang, H, Gao, Q and Leung, V. Joint energy management and interference coordination with Max-Min fairness in ultra-dense hetnets. IEEE Access, Vol. 6, 32588-32600. 

[3]    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. 

[4]    Li, X and Zhu, D (2018) Robust feature selection via l 2, 1 -norm in finite mixture of regression. Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2018.02.021.

[5]    Wang, L, Zhu, D and Dong, M (2018) Clustering over-dispersed data with mixed feature types. Statistical Analysis and Data mining, 11(2), 55-65, April 2018.

[6]    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 Source Code.

[7]    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.

[8]    Wang, L, Zhu, D*, Li, Y and Dong, M (2017) Modeling Over-dispersion for Network Data Clustering. In the proceeding of 16th IEEE International Conference on Machine Learning and Application (ICMLA’17). (Best Paper Award Top 3 Finalist, *Corresponding Autor) 

[9]    Nezhad, MZ, Zhu, D*, Yang, K and Levy, P. (2017) A Supervised Bi-Clustering Approach for Precision Medicine. In the proceeding of 16th IEEE International Conference on Machine Learning and Application (ICMLA’17).  (Best Poster Award Top 3 Finalist, *Corresponding Autor)

[10]    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)

[11]    Wang, L, Li, Y, Zhou, J, Zhu, D and Ye, J (2017) Multi-task Survival Analysis. In the proceedings of 2017 IEEE International Conference on Data Mining (ICDM’17)

[12]    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 Information Association (AMIA) Summit on Clinical Research Informatics, San Francisco, March 2017.

[13]    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). 

[14]    Xu, H, Dong, M, Zhu, D, et al. (2016) Text Classification with Topic-based Word Embedding and Convolutional Neural Networks. In the proceedings of 2016 ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2016).

[15]    Wang, L, Zhu, D, Li, Y and Dong, M. (2016) Poisson-Markov Mixture Model and Parallel Algorithm for Binning Massive and Heterogeneous DNA Sequencing Reads. In the Series of Lecture Notes in Computer Science (ISBRA 2016).

[16]    Almomani, R, Dong, M and Zhu, D. (2016) Bayesian Hierarchical Appearance Model for Robust Object Tracking. In the Proceeding of International Conference on Multimedia and Expo (ICME 2016). 

[17]    Almomani, R, Dong, M and Zhu, D. (2016) Object Tracking via Dirichlet Process-based Appearance Models. Neural Computing and Applications, in press. DOI: 10.1007/s00521-016-2280-1.

科研项目：

1.   NSF/CCF: S&CC: Promoting a Healthier Urban Community: Prioritization of Risk Factors for the Prevention and Treatment of Pediatric Obesity. 09/01/2016-08/31/2019. (co-Principal Investigator) 

2.   NSF/IIS: S&AS: INT: Autonomous Battery Operating System (ABOS): An Adaptive and Comprehensive Approach to Efficient, Safe, and Secure Battery System  Management. 09/01/2017-08/31/2021. (Senior Personnel)

3.   NSF/CCF:  EAGER: A novel algorithmic framework for discovering subnetworks from big biological data. 08/15/2014-08/14/2017. (Principal Investigator)

4.   NIH/NLM:  R21.A new informatics paradigm for reconstructing signaling pathways in human disease. 09/2009 – 08/2012. (Principal Investigator)

5.   NIH/NCI: R01. Analysis of Epstain-Barr virus type III latency on cellular miRNA gene expression. (co-Investigator)

6.   NSF/CCF: CPATH: A verification based learning model that enriches CS and related undergraduate programs. (co-Principal Investigator)

教学情况：