朱冬霄

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职称:教授/硕士生导师

个人简介:

主要研究领域:机器学习和数据科学原创性研究,及其在生物医学信息学,健康信息学,多媒体及文本大数据里的应用。

科研成果:

[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:

Classroom Teaching (Computer Science Department at Wayne State University):

1.   CSC 8860 Seminar Topics in Computer Vision and Pattern Recognition. Fall 2017

2.   CSC 7825 Machine Learning. Fall 2014, Fall 2015, Fall 2016

3.   CSC 6580 Design and Analysis of Algorithms. Winter 2015, Winter 2016, Winter 2017 

4.   CSC 5825 Intro. to Machine Learning and Applications. Winter 2017, Fall 2017, Fall 2018

5.   CSC 5991 Special Topics in Comp. Sci. Fall 2012, Fall 2013, Winter 2014 , Winter 2015, Winter 2016

6.   CSC 2110 Comp. Sci. I (C++ Programming). Fall 2011, Winter 2012, Winter 2013, Fall 2013 


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