王贝伦
发布人:王霞  发布时间:2019-01-05   浏览次数:1807

Beilun Wang's Personal Webpage

Email:

beilun@seu.edu.cn

Short Bio

I am an associate professor in Southeast University, China. Before join SEU, I was a Ph.D. student in the Department of Computer Science at the University of Virginia, working with Prof. Yanjun Qi . I was also a member of UVA Machine Learning and Biomedicine Group. My research interest is Machine learning, especially Graphical Model and Adversarial Machine Learning. Before that I received my B.S. from Department of MathematicsNanjing University, Nanjing in June of 2013.

Education

Present:

Ph.D. in Computer Science Department of Computer Science , in University of Virginia.

2013 June:

I received my B.S. in June of 2013 from Department of MathematicsNanjing University, Nanjing.

Publications (not updated)

Submit to AISTAT 2018

Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure,
Beilun Wang, Arshdeep Sekhon, Yanjun Qi ( link)

ICLR17 Workshop

A Theoretical Framework for Robustness of (DEEP) Classifier Against Adversarial Samples,
Beilun Wang, Ji Gao, Yanjun Qi ( link)

ICML 2016 WCB 
(Workshop on Computational Biology) 
Spotlights Talk
Travel Award (4 out of 50)

A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models,
Beilun Wang, Rita Singh, Yanjun Qi ( link)

AISTAT 2017

A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models,
Beilun Wang, Ji Gao, Yanjun Qi

Preprint

A Constrained, Weighted-`1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs,
Chandan Singh, Beilun Wang, Yanjun Qi ( link)

PSB 2017

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks,
Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi ( link)

ACM-TKDD 2016

Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data,
Feiyu Xiong, Moshe Kam, Leonid Hrebien, Beilun Wang, Yanjun Qi ( link)