Education

2019-Present

Postdoc Research Fellow; Simons Foundation (New York, USA)

2014-2019

Ph.D. in Bioinformatics; UCLA (Los Angeles, USA)

Thesis: Computational methods to elucidate post-transcriptional gene regulation using high-throughput sequencing data

2015-2019

M.S. in Statistics:; UCLA (Los Angeles, USA)

Thesis: Neural Architecture Search for Biological Sequences

2010-2014

B.Sc., Zhejiang University (Hangzhou, China)

Publications

• Zijun Zhang, Zhicheng Pan, Yi Ying, Zhijie Xie, Samir Adhikari, John Phillips, Russ P Carstens, Douglas L Black, Yingnian Wu, Yi Xing; Deep-learning augmented RNA-seq analysis of transcript splicing (Nature Methods, 2019)
• Zijun Zhang, Linqi Zhou, Liangke Gou, Yingnian Wu; Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge (arXiv, 2019)
• Zhang, Zijun; Park, Eddie; Lin, Lan; Xing, Yi; A panoramic view of RNA modifications: exploring new frontiers (Genome Biology, 2018)
• Park, Eddie; Pan, Zhicheng; Zhang, Zijun; Lin, Lan; Xing, Yi; The expanding landscape of alternative splicing variation in human populations (American Journal of Human Genetics, 2018)
• Zhang, Zijun; Xing, Yi; CLIP-seq analysis of multi-mapped reads discovers novel functional RNA regulatory sites in the human transcriptome (Nucleic acids research, 2017)
• Liu, Lili; Zhang, Zijun; Sheng, Taotao; Chen, Ming; DEF: an automated dead-end filling approach based on quasi-endosymbiosis (Bioinformatics, 2017)
• Liu, Lili; Mei, Qian; Yu, Zhenning; Sun, Tianhao; Zhang, Zijun; Chen, Ming; An integrative bioinformatics framework for genome-scale multiple level network reconstruction of rice (Journal of integrative bioinformatics,2013)
• Liu, Lili; Zhang, Zijun; Mei, Qian; Chen, Ming; PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction (PLoS One, 2013)

Projects

BioNAS

Neural architecture search for Bioinformatics

AutoML framework for designing powerful and interpretable neural networks.

https://github.com/zj-zhang/BioNAS-pub

CLAM

CLIP-seq Analysis of Multi-mapped reads

Open-source python package for calling peaks in CLIP/RIP-seq data.

https://github.com/Xinglab/CLAM

DARTS

Deep learning augmented Analysis of RNA-seq Transcript Splicing

Bayesian inference utilizing deep-learning prediction informative prior

https://github.com/Xinglab/DARTS

Skills

• Programming languages: Python, R, scikit, Keras, Tensorflow, Theano, Matlab, AWS, HTML, CSS, PHP, Jekyll, SQL, C/C++, Unix/SGE, Git
• Statistics: Regularized regression, Bayesian statistics and Bayesian inference, Causal Inference, Hierarchical modeling, MCMC, multivariate analysis
• Machine/Deep learning: Supervised(CNN, LSTM, adaboost/xgboost, SVM, random forest), Unsupervised(clustering, autoencoder, factor analysis), Reinforcement(controller network)