基本信息
陈 渤：教授 、博导
博士学科：信号与信息处理
硕士学科：信号与信息处理　
工作单位：电子工程学院雷达信号处理国家重点实验室（电子所）、信息感知集成攻关研究院
信息感知集成攻关研究院副主任、信息与通信工程学部副主任、“111”引智基地执行主任

联系方式
通信地址：西安电子科技大学雷达信号处理国家重点实验室，西安市，陕西省，710071
电子邮箱：bchen@mail.xidian.edu.cn
办公电话：
办公地点：100号楼411


个人简介
分别于2003年、2006年和2008年获西安电子科技大学电子工程专业学士学位、信号与信息处理专业硕士学位和博士学位。
2010年获得全国百篇优秀博士学位论文提名奖。
2008年11月至2012年12月在美国杜克大学电子计算机工程系担任postdoc research fellow，research scientist以及senior research scientist。
2013年加入西电。
2014年入选中组部高层次人才引进计划。
Associate Editor, IEEE Transactions on Signal Processing

研究方向
主要研究方向：机器学习、统计信号处理、雷达目标识别与检测、深度学习网络、大规模数据处理。

本科生、研究生、博士后招生要求
1、每年将招收1-2名博士，欢迎highly motivated的学生报考。对于研究生推免生，优先考虑有硕博连读志向的申请者。
2、踏实、认真、严谨，对工作与学习有较强的积极主动性；
3、在编程、数学、信号处理、光学成像系统等任一方面有较深的功底和较浓的兴趣；
4、本科生需要在“学有余力”的情况下，对科研有兴趣，并希望继续深造；
5、博士后需要在机器学习、信号处理等领域的权威期刊和会议发表过相关文章。
具有较强编程经验的考研学生将被优先考虑，欢迎报考本组研究生。

新闻动态
恭喜郭丹丹和郑美曦文章，SAR Automatic Target Recognition based on Supervised Deep Variational Auto-encoding Model，被IEEE Trans. on AES录用，用概率深度网络提升SAR目标识别的可解释性。
恭喜段志斌（研二）和王东升的文章，Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network，被ICML 2021接收，提升多层神经元相关性，进一步可量化不同层神经元间的语义距离。也恭喜Shujian和Xinjie的文章，Bayesian Attention Belief Networks，被录用，将注意力机制贝叶斯化。
恭喜段志斌（研二）和张昊的文章，EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering，被ACL 2021录用。
恭喜程子恒（研二）的文章，Memory-Efficient Network for Large-scale Video Compressive Sensing，被IEEE CVPR2021录用，解决了SCI处理大规模数据时存储空间负担重的问题。
恭喜王正珏和张昊的文章，MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing，被IEEE CVPR2021录用，基于meta-learning的Video Compressive Sensing。
恭喜陈文超的文章，Tensor RNN with Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition，被IEEETransactions on Signal Processing录用，提出混合Tensor RNN学习雷达HRRP内部特征的变化。
被授予2021年度陕西省青年科技标兵。
恭喜陈文超的文章，Max-Margin Deep Diverse Latent Dirichlet Allocation with continual learning，被IEEE Transactions on Cybernetics录用。
恭喜郭丹丹的文章，Variational Temporal Deep Generative Model for Radar HRRP Target Recognition，被IEEE Transactions on Signal Processing录用，提出动态概率深度生成模型建模雷达HRRP。
恭喜王正珏的文章，Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational Probabilistic Generative Model被IEEE Trans. on Neural Networks and Learning Systems录用。
恭喜王正珏和段志斌的文章，Friendly Topic Assistant for Transformer Based Abstractive Summarization，被EMNLP2020录用。
雷达信号处理国家级重点实验室、111引智基地和信息感知集成攻关研究院主办的第三届SIILP（International Workshop on Signal and Information Intelligent Learning and processing）将在8月15-16日在腾讯会议和VooV会议召开。
恭喜文伟、张学峰和王正珏文章，Infinite Bayesian Max-Margin Discriminant Projection，被IEEE Transactions on Cybernetics录用。
恭喜鲁瑞颖和程子恒的文章，RAFnet: Recurrent Attention Fusion Network of Hyperspectral and Multispectral images，被Signal Processing录用。
恭喜程子恒和鲁瑞颖的文章，BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging，被计算机视觉会议ECCV2020录用！所提技术在Video Snapshot Compressive Imaging任务仿真和实测公开数据上得到了state of the art的结果。
恭喜张昊的文章，Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference，被IEEE Trans. on Pattern Analysis and Machine Intelligence录用！
恭喜王正珏和鲁瑞颖的文章，FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion，被IEEE Trans. on Image Processing录用！
恭喜郭丹丹和鲁瑞颖的文章，Recurrent Hierarchical Topic-Guided RNN for Language Generation，被机器学习顶级会议ICML2020录用！
恭喜陈文超和本科实习生刘毅成的工作被CCF A类会议IJCAI2020录用！恭喜田隆和彭杨的文章被遥感会议IGARSS2020录用并且入选oral presentation！
恭喜张昊和田隆的文章，Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling，被深度学习顶级会议ICLR2020录用。
12月18日-12月20日，参加MIIS2019并做Deep Probabilistic Model and Its Applications的报告，介绍我们近几年在概率深度网络方面的工作。
从7月1日起，担任IEEETransactions on SignalProcessing的AssociateEditor。
恭喜王超杰和肖肃诚的文章，Convolutional Poisson Gamma Belief Network，被机器学习顶级会议ICML2019录用，可处理带有order的one-hot数据的probabilisticconvolutionalneural network，不需要进行wordembedding预处理，end-to-end的概率生成模型。Code已经release！
恭喜徐彬、王正珏和杜川的文章分别被Signal Processing录用！
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling，（ICLR2018）的code已经release！
恭喜郭丹丹和张昊的文章，Deep Poisson Gamma Dynamic Systems，被机器学习顶级会议NIPS2018 录用。
恭喜张昊的文章，DeepMax-Margin Discriminant Projection，被IEEETransactions on Cybernetics 录用。
恭喜张昊和郭丹丹的文章，WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling，被深度学习顶级会议International Conference on Learning Representations (ICLR2018) 录用。该工作向构建实用的深度概率生成模型又迈出了重要一步，兼具着传统深度网络的学习高效性与概率模型的可解释性和不确定性，可处理所有非负数据问题，尤其是topic modeling。有兴趣可以关注我们分别在NIPS2015、JMLR2016和ICML2017发表的系列工作。
恭喜王超杰的文章，Multimodal Poisson Gamma Belief Network，被AAAI2018录用
第二届电子信息青年****论坛（EIES2017）顺利在西安电子科技大学召开，会议详细内容，包括日程、特邀专家****的slides，都可以在EIES2017大会主页下载：http://meeting.xidian.edu.cn/symposium/eies2017/。
恭喜王正珏和翟颖获得国家奖学金
恭喜丛玉来加入ECE Department of Duke University as a postdoc.
Program Committee memberfor NIPS 2017
丛玉来的文章，Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian (TLASGR) MCMC，被International Coference on Machine Learning (ICML) 2017录用。恭喜！A stochasticonline deep probabilistic generative model.
丛玉来的文章，Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions，被Bayesian Analysis录用。恭喜！
2016电子信息青年****论坛（EIES2016）11.12-13日在西安电子科技大学召开，由雷达信号处理国家重点实验室主办。欢迎大家来参会，会议免费注册：http://meeting.xidian.edu.cn/symposium/eies2016/
恭喜张昊、万锦伟、王超杰、田隆获得航天三院举办的雷达目标识别竞赛第二名！
受邀在复旦大学召开的第二届《雷达学报》青年科学家论坛做SAR图像处理研究相关报告。
冯博的文章，Radar HRRP target recognition with deep networks，被Pattern Recognition刊录，虽然经过了两年的review。恭喜！
受邀在第二届成都大数据技术与产业应用大会做主题报告。
丛玉来的文章，Nonparametric Bayesian Attributed Scattering Center Extraction for Synthetic Aperture Radar Targets，被IEEE Transactions on Signal Processing录用。恭喜！
张学锋与丁军的两篇文章分别被Pattern Recognition和GRSL录用。恭喜！
加拿大蒙特利尔参加NIPS2015.
受邀在雷达前沿技术论坛做大会报告。
受邀在南京信息工程大学和雷达学报青年****论坛做大会报告。
张昊与丁艳华分别获得博士与硕士国家奖学金。恭喜！
与University of Texas at Austin的Mingyuan Zhou教授合作论文“The Poisson Gamma Belief Network”被NIPS2015录用，a fully Bayesian deep generative model。
获得*科研项目*
成功举办第四届MIIS国际会议。明年将在北京举行。
受邀在中国机器学习大会（MLA2015）做大会报告

---

基本信息
陈 渤：教授 、博导
博士学科：信号与信息处理
硕士学科：信号与信息处理　
工作单位：电子工程学院雷达信号处理国家重点实验室（电子所）、信息感知集成攻关研究院
信息感知集成攻关研究院副主任、信息与通信工程学部副主任、“111”引智基地执行主任

联系方式
通信地址：西安电子科技大学雷达信号处理国家重点实验室，西安市，陕西省，710071
电子邮箱：bchen@mail.xidian.edu.cn
办公电话：
办公地点：100号楼411


个人简介
分别于2003年、2006年和2008年获西安电子科技大学电子工程专业学士学位、信号与信息处理专业硕士学位和博士学位。
2010年获得全国百篇优秀博士学位论文提名奖。
2008年11月至2012年12月在美国杜克大学电子计算机工程系担任postdoc research fellow，research scientist以及senior research scientist。
2013年加入西电。
2014年入选中组部高层次人才引进计划。
Associate Editor, IEEE Transactions on Signal Processing

研究方向
主要研究方向：机器学习、统计信号处理、雷达目标识别与检测、深度学习网络、大规模数据处理。

本科生、研究生、博士后招生要求
1、每年将招收1-2名博士，欢迎highly motivated的学生报考。对于研究生推免生，优先考虑有硕博连读志向的申请者。
2、踏实、认真、严谨，对工作与学习有较强的积极主动性；
3、在编程、数学、信号处理、光学成像系统等任一方面有较深的功底和较浓的兴趣；
4、本科生需要在“学有余力”的情况下，对科研有兴趣，并希望继续深造；
5、博士后需要在机器学习、信号处理等领域的权威期刊和会议发表过相关文章。
具有较强编程经验的考研学生将被优先考虑，欢迎报考本组研究生。

新闻动态
恭喜郭丹丹和郑美曦文章，SAR Automatic Target Recognition based on Supervised Deep Variational Auto-encoding Model，被IEEE Trans. on AES录用，用概率深度网络提升SAR目标识别的可解释性。
恭喜段志斌（研二）和王东升的文章，Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network，被ICML 2021接收，提升多层神经元相关性，进一步可量化不同层神经元间的语义距离。也恭喜Shujian和Xinjie的文章，Bayesian Attention Belief Networks，被录用，将注意力机制贝叶斯化。
恭喜段志斌（研二）和张昊的文章，EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering，被ACL 2021录用。
恭喜程子恒（研二）的文章，Memory-Efficient Network for Large-scale Video Compressive Sensing，被IEEE CVPR2021录用，解决了SCI处理大规模数据时存储空间负担重的问题。
恭喜王正珏和张昊的文章，MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing，被IEEE CVPR2021录用，基于meta-learning的Video Compressive Sensing。
恭喜陈文超的文章，Tensor RNN with Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition，被IEEETransactions on Signal Processing录用，提出混合Tensor RNN学习雷达HRRP内部特征的变化。
被授予2021年度陕西省青年科技标兵。
恭喜陈文超的文章，Max-Margin Deep Diverse Latent Dirichlet Allocation with continual learning，被IEEE Transactions on Cybernetics录用。
恭喜郭丹丹的文章，Variational Temporal Deep Generative Model for Radar HRRP Target Recognition，被IEEE Transactions on Signal Processing录用，提出动态概率深度生成模型建模雷达HRRP。
恭喜王正珏的文章，Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational Probabilistic Generative Model被IEEE Trans. on Neural Networks and Learning Systems录用。
恭喜王正珏和段志斌的文章，Friendly Topic Assistant for Transformer Based Abstractive Summarization，被EMNLP2020录用。
雷达信号处理国家级重点实验室、111引智基地和信息感知集成攻关研究院主办的第三届SIILP（International Workshop on Signal and Information Intelligent Learning and processing）将在8月15-16日在腾讯会议和VooV会议召开。
恭喜文伟、张学峰和王正珏文章，Infinite Bayesian Max-Margin Discriminant Projection，被IEEE Transactions on Cybernetics录用。
恭喜鲁瑞颖和程子恒的文章，RAFnet: Recurrent Attention Fusion Network of Hyperspectral and Multispectral images，被Signal Processing录用。
恭喜程子恒和鲁瑞颖的文章，BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging，被计算机视觉会议ECCV2020录用！所提技术在Video Snapshot Compressive Imaging任务仿真和实测公开数据上得到了state of the art的结果。
恭喜张昊的文章，Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference，被IEEE Trans. on Pattern Analysis and Machine Intelligence录用！
恭喜王正珏和鲁瑞颖的文章，FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion，被IEEE Trans. on Image Processing录用！
恭喜郭丹丹和鲁瑞颖的文章，Recurrent Hierarchical Topic-Guided RNN for Language Generation，被机器学习顶级会议ICML2020录用！
恭喜陈文超和本科实习生刘毅成的工作被CCF A类会议IJCAI2020录用！恭喜田隆和彭杨的文章被遥感会议IGARSS2020录用并且入选oral presentation！
恭喜张昊和田隆的文章，Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling，被深度学习顶级会议ICLR2020录用。
12月18日-12月20日，参加MIIS2019并做Deep Probabilistic Model and Its Applications的报告，介绍我们近几年在概率深度网络方面的工作。
从7月1日起，担任IEEETransactions on SignalProcessing的AssociateEditor。
恭喜王超杰和肖肃诚的文章，Convolutional Poisson Gamma Belief Network，被机器学习顶级会议ICML2019录用，可处理带有order的one-hot数据的probabilisticconvolutionalneural network，不需要进行wordembedding预处理，end-to-end的概率生成模型。Code已经release！
恭喜徐彬、王正珏和杜川的文章分别被Signal Processing录用！
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling，（ICLR2018）的code已经release！
恭喜郭丹丹和张昊的文章，Deep Poisson Gamma Dynamic Systems，被机器学习顶级会议NIPS2018 录用。
恭喜张昊的文章，DeepMax-Margin Discriminant Projection，被IEEETransactions on Cybernetics 录用。
恭喜张昊和郭丹丹的文章，WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling，被深度学习顶级会议International Conference on Learning Representations (ICLR2018) 录用。该工作向构建实用的深度概率生成模型又迈出了重要一步，兼具着传统深度网络的学习高效性与概率模型的可解释性和不确定性，可处理所有非负数据问题，尤其是topic modeling。有兴趣可以关注我们分别在NIPS2015、JMLR2016和ICML2017发表的系列工作。
恭喜王超杰的文章，Multimodal Poisson Gamma Belief Network，被AAAI2018录用
第二届电子信息青年****论坛（EIES2017）顺利在西安电子科技大学召开，会议详细内容，包括日程、特邀专家****的slides，都可以在EIES2017大会主页下载：http://meeting.xidian.edu.cn/symposium/eies2017/。
恭喜王正珏和翟颖获得国家奖学金
恭喜丛玉来加入ECE Department of Duke University as a postdoc.
Program Committee memberfor NIPS 2017
丛玉来的文章，Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian (TLASGR) MCMC，被International Coference on Machine Learning (ICML) 2017录用。恭喜！A stochasticonline deep probabilistic generative model.
丛玉来的文章，Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions，被Bayesian Analysis录用。恭喜！
2016电子信息青年****论坛（EIES2016）11.12-13日在西安电子科技大学召开，由雷达信号处理国家重点实验室主办。欢迎大家来参会，会议免费注册：http://meeting.xidian.edu.cn/symposium/eies2016/
恭喜张昊、万锦伟、王超杰、田隆获得航天三院举办的雷达目标识别竞赛第二名！
受邀在复旦大学召开的第二届《雷达学报》青年科学家论坛做SAR图像处理研究相关报告。
冯博的文章，Radar HRRP target recognition with deep networks，被Pattern Recognition刊录，虽然经过了两年的review。恭喜！
受邀在第二届成都大数据技术与产业应用大会做主题报告。
丛玉来的文章，Nonparametric Bayesian Attributed Scattering Center Extraction for Synthetic Aperture Radar Targets，被IEEE Transactions on Signal Processing录用。恭喜！
张学锋与丁军的两篇文章分别被Pattern Recognition和GRSL录用。恭喜！
加拿大蒙特利尔参加NIPS2015.
受邀在雷达前沿技术论坛做大会报告。
受邀在南京信息工程大学和雷达学报青年****论坛做大会报告。
张昊与丁艳华分别获得博士与硕士国家奖学金。恭喜！
与University of Texas at Austin的Mingyuan Zhou教授合作论文“The Poisson Gamma Belief Network”被NIPS2015录用，a fully Bayesian deep generative model。
获得*科研项目*
成功举办第四届MIIS国际会议。明年将在北京举行。
受邀在中国机器学习大会（MLA2015）做大会报告

---

科学研究
目前研究团队承担的科研项目（可公开的信息如下）：
1、国家自然科学基金（面上项目）：基于分层超完备字典稀疏表示的深度学习算法研究及应用，2014-2017.
2、新世纪优秀人才支持计划：2014-2016.
3、协同创新中心自设项目：2015-2018.
4、国家自然基金（面上项目）：2018-2021
5、多项横向项目

---

Selected Publications
Dandan Guo, Bo Chen*, Meixi Zheng andHongwei Liu, SAR Automatic Target Recognition based on Supervised Deep Variational Auto-encoding Model，to appear in IEEE Transactions on Aerospace and Electronic Systems, 2021.
Zhibin Duan, Dongsheng Wang, Bo Chen*, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren and Mingyuan Zhou, Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network,to appear inInternational Conference on Machine Learning (ICML) 2021.
Shujian Zhang, Xinjie Fan, Bo Chen and Mingyuan Zhou,Bayesian Attention Belief Networks, to appear inInternational Conference on Machine Learning (ICML) 2021.
Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang,Bo Chen*, and Mingyuan Zhou:EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering, to appear in the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) Bangkok, Thailand, 1-6 August 2021.
Chaojie Wang, Bo Chen*,Sucheng Xiao, Zhengjue Wang, Hao Zhang, Penghui Wang, Ning Han, and Mingyuan Zhou,Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data,accepted by IEEE Transactions on Cybernetics, 2021.
Wenchao Chen, Bo Chen*,Xiaojun Peng, Jiaqi Liu, Yang Yang, Hao Zhang and Hongwei Liu,Tensor RNN with Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition, in IEEE Transactions on Signal Processing, Vol. 69, pp. 1995-2009, 2021.
ZihengCheng, BoChen*, GuanliangLiu, HaoZhang, RuiyingLu, ZhengjueWang and X. Yuan*, Memory-Efficient Network for Large-scale Video Compressive Sensing, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
ZhengjueWang, HaoZhang, ZihengCheng, BoChen* and XinYuan*, MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing,IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Wenchao Chen, Xuefei Cao, Bo Chen*,Yingqi Liu, Qianru Zhao, Hao Zhang,Max-Margin Deep Diverse Latent Dirichlet Allocation with continual learning,accepted by IEEE Transactions on Cybernetics, 2021.
Wenchao Chen,Chaojie Wang, Bo Chen*, Yicheng Liu, Hao Zhangand Mingyuan Zhou,Bidirectional Convolutional Poisson Gamma Dynamical Systems,to appear inAdvances in Neural Information Processing Systems (NeurIPS), Virtual,2020. [Code]
Chaojie Wang, Hao Zhang, Bo Chen*, Dongsheng Wang, Zhengjue Wang and Mingyuan Zhou,Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network,to appear inAdvances in Neural Information Processing Systems (NeurIPS), Virtual,2020. [Code]
Xinjie Fan, Shujian Zhang, Bo Chen and Mingyuan Zhou, Bayesian Attention Modules,to appear inAdvances in Neural Information Processing Systems (NeurIPS), Virtual,2020
Dandan Guo, Bo Chen*, Wenchao Chen, Chaojie Wang, Hongwei Liu, and Mingyuan Zhou,Variational Temporal Deep Generative Model for Radar HRRP Target Recognition, to appear inIEEE Transactonson Signal Processing, 68, 5795-5809, 2020.
Zhengjue Wang, Bo Chen*, Hao Zhang, and Hongwei Liu, Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational Probabilistic Generative Model, to appear in IEEE Transactions on Neural Networks and Learning Systems 2020.
Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen*and Mingyuan Zhou, Friendly Topic Assistant for Transformer Based Abstractive Summarization, to appear in the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP2020, Dominican Republic, November, 2020. [Code]
Wei Wen, Bo Chen*, Xuefei Cao*, Xuefeng Zhang, Zhengjue Wang and Hongwei Liu, Infinite Bayesian Max-Margin Discriminant Projection, to appear in IEEE Transactions on Cybernetics 2020.
Ruiying Lu, Bo Chen*, Ziheng Cheng andPenghui Wang*, RAFnet: Recurrent Attention Fusion Network of Hyperspectral and Multispectral images, to appear in Signal Processing 2020.
Ziheng Cheng, Ruiying Lu, Zhengjue Wang, Hao Zhang, Bo Chen*, Ziyi Meng and Xin Yuan*, BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging, to appear in European Conference on Computer Vision (ECCV), Glasgow, August 2020. [Code]
Hao Zhang, Bo Chen*, Yulai Cong, Dandan Guo, Hongwei Liu, and Mingyuan Zhou，Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference, to appear in IEEE Trans. on Pattern Analysis and Machine Intelligence.
Zhengjue Wang, Bo Chen*, Ruiying Lu, Hao Zhang, Hongwei Liu, and Pramod K. Varshney, FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion, IEEE Trans. on Image Processing, Vol.29. 7565-7577, 2020.
Dandan Guo,Bo Chen*, Ruiying Lu and Mingyuan Zhou,Recurrent Hierarchical Topic-Guided RNN for Language Generation, Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3810-3821, Vienna, Austria, July 2020.
Wenchao Chen, Bo Chen*, Yicheng Liu, Qianru Zhao, Mingyuan Zhou, Switching Poisson Gamma Dynamical Systems, to appear in the 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, July2020.
Zhengjue Wang, Chaojie Wang, HaoZhang, ZhibinDuan, MingyuanZhou, and BoChen*, Learning dynamic hierarchical topic graph with graph convolutional network for document classification,International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy, June 2020.
Hao Zhang, Bo Chen*,Long Tian, Zhengjue Wang and Mingyuan Zhou,Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling,in International Conference on Learning Representations (ICLR), Addis Ababa, ETHIOPIA, May 2020. [Code]
Chaojie Wang, Bo Chen*, Sucheng Xiao, and Mingyuan Zhou, Convolutional Poisson Gamma Belief Network, to appear in International Conference on Machine Learning (ICML), 2019: 6515-6525, Long Beach, CA, USA, June2019. [Code]
Jinwei Wan, Bo Chen*,Bin Xu, Hongwei Liu and Lin Jin, Convolutional Neural Networks for Radar HRRP Target Recognition and Rejection,EURASIP, 2019(1):5, 2019.
Chuan Du, Bo Chen*,Hongwei Liuand Bin Xu, Factorized Discriminative Conditional Variational Auto-encoder for Radar HRRP Target Recognition, Signal Processing, 2019(158):176–189.
Zhengjue Wang,Bo Chen*, Hao Zhang and Hongwei Liu, Variational Probabilistic Generative Framework for Single Image Super-Resolution, Signal Processing,2019: 92-105.
Bin Xu,Bo Chen*, Jinwei Wan, Hongwei Liu, and Lin Jin, Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition, Signal Processing, 2019: 268-280.
Dandan Guo,Bo Chen*,Hao Zhang,and Mingyuan Zhou,Deep Poisson Gamma Dynamic Systems, to appear inAdvances in Neural Information Processing Systems (NIPS), 2018: 8442-8452, Montreal, Canada.
Hao Zhang,Bo Chen*, Zhengjue Wang, and Hongwei Liu, Deep Max-Margin Discriminant Projection, IEEE Transactions on Cybernetics, Vol. 49, No. 7, 2454-2466, July2019.
Hao Zhang, Bo Chen*, Dandan Guo, and Mingyuan Zhou, WHAI: Weibull hybrid autoencoding inference for deep topic modeling, to appear in International Conference on Learning Representations (ICLR), Vancouver, Canada, May 2018. [Code]
Chaojie Wang, Bo Chen*, Hongwei Liu, and Mingyuan Zhou, Multimodal Poisson Gamma Belief Network, to appear in the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Lousiana, USA, Feburary 2018. [Code]
Yulai Cong, Bo Chen*, Hongwei Liu, and Mingyuan Zhou, Deep latent Dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC, in International Conference on Machine Learning (ICML), PMLR 70: 864-873, Sydney, Australia, August 2017.
Jing Chai, Bo Chen, Fan Liu, Zehua Chen, Xinghao Ding. Multiple-Instance Feature Extraction at the Bag and Instance Levels Using the Maximum-Trace Difference Criterion. Information Sciences: 2017 ,385-386 ,353-377.
Yulai Cong, Bo Chen*, and Mingyuan Zhou, Fast simulation of hyperplane-truncated multivariate normal distributions, Bayesian Analysis, Vol. 12, No. 4, pp: 1017-1037, 2017. [Code]
Bo Feng,Bo Chen*and Hongwei Liu,Radar HRRP target recognition with deep networks,Pattern Recognition 61, 379-393, 2017.
Mingyuan Zhou*,Yulai Cong, andBo Chen*,Augmentable gamma belief networks, Journal of Machine Learning Research,17(163), 1-44, 2016. [Code]
Yulai Cong, Bo Chen*, Hongwei Liu, Bo Jiu. Nonparametric Bayesian Attributed Scattering Center Extraction for Synthetic Aperture Radar Targets. IEEE Transactions on Signal Processing, 64(18), 4723-4736, 2016.
Jun Ding, Bo Chen*, Hongwei Liu, Mengyuan Huang, Convolutional Neural Network With Data Augmentation for SAR Target Recognition, IEEE Geoscience and Remote Sensing Letters, 13(3), 364-368, 2016.
Xuefeng Zhang, Bo Chen*, Hongwei Liu, Lei Zuo, and Bo Feng, Infinite max-margin factor analysis via data augmentation. Pattern Recognition 52, 17-32, 2016.
Hongwei Liu, Bo Feng, Bo Chen*, Lan Du, Radar high-resolution range profiles target recognition based on stable dictionary learning, IET Radar, Sonar & Navigation, 10(2), 228–237, 2016.
Mingyuan Zhou, Yulai Cong, Bo Chen, The Poisson Gamma Belief Network, Advances in Neural Information Processing Systems (NIPS). 3025-3033, 2015, Montreal, Canada.
Junkun Yan, Hongwei Liu, Bo Jiu,Bo Chen, Zheng Liu, Zheng Bao:Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking. IEEE Transactions on Signal Processing, 63(12): 3110-3122, 2015. (Impact Factor: 3.2)
Bo Chen, Hao Zhang, Xuefeng Zhang, Wei Wen, Hongwei Liu and Jun Liu, Max-Margin Discriminant Projection via Data Augmentation, IEEE Transactions on Knowledge and Data Engineering,27(7), 1964-1976, 2015.(Impact Factor: 1.8)
Bo Jiu, Hongwei Liu, Xu Wang, Lei Zhang, Yinghua Wang, Bo Chen, Knowledge-Based Spatial-Temporal Hierarchical MIMO Radar Waveform Design Method for Target Detection in Heterogeneous Clutter Zone, IEEE Transactions on Signal Processing, 63(3), 543-554, 2015.(Impact Factor: 3.2)
Junkun Yan, Bo Jiu, Hongwei Liu, Bo Chen, Zheng Bao: Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter. IEEE Transactions on Signal Processing, 63(2): 512-527, 2015.(Impact Factor: 3.2)
Raymond J. Langley, Ephraim L. Tsalik, Jennifer C. vanVelkinburgh, Seth W. Glickman, Brandon J. Rice, Chunping Wang,BoChen, Lawrence Carin, ..., Christopher W. Woods, Stephen F.Kingsmore, An integrated clinico-me[ant]tabolomic model improves prediction of death in sepsis, Science Translational Medicine, Vol. 5, Issue 195, p. 195ra95, July, 2013.(Impact Factor: 10.8)
Bo Jiu, Hongwei Liu, Bo Chen, Zheng Liu, Waveform Design for Wideband Radar Target Recognition Based on Eigensubspace Projection, IET Radar, Sonar & Navigation,7(6), 702–709, 2013.(Impact Factor: 1.0)
Bo Chen, Gungor Polatkan, Guillermo Sapiro, David Blei, David Dunson and Lawrence Carin, Deep Learning with Hierarchical Convolutional Factor Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence,35(8), 1887-1901, 2013.(Impact Factor: 4.908)
Bo Chen, David E. Carlson and Lawrence Carin, On the Analysis of Multi-Channel Neural Spike Data, Neural Information Processing Systems (NIPS) 2011, Granada, Spain.
Bo Chen, Guillermo Sapiro, Gungor Polatkan, David Dunson and Lawrence Carin, The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning, International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA. [online video: http://techtalks.tv/talks/54368/]
Bo Chen, Guillermo Sapiro, David Dunson, Lawrence Carin, Deep Networks with Hierarchical Convolutional Factor Analysis, Neural Information Processing Systems (NIPS) 2010 Workshop on Deep Learning and Unsupervised Feature Learning, Vancouver, Canada.
Bo Chen, Minhua Chen, John Paisley, Aimee Zaas, Christopher Woods, Geoff Ginsburg, Alfred Hero III, Joseph Lucas, David Dunson and Lawrence Carin. Bayesian inference of the number of factors in gene-ex[ant]pression analysis: application to human virus challenge studies. BMC Bioinformatics, Volume 11, Number 1, 552, 2010.(Impact Factor: 3.028)
Bo Chen, John Paisley and Lawrence Carin. Sparse Linear Regression with Beta Process Priors, ICASSP 2010, Dallas, USA.
Jing Chai, Hongwei Liu, Bo Chen and Zheng Bao. Large margin nearest local mean classifier. Signal Processing, Volume 90 Issue 1, January, 2010.(Impact Factor: 1.503)
Bo Chen, Hongwei Liu, Jing Chai and Zheng Bao. Large Margin Feature Weighting via Linear Programming. IEEE Transactions on Knowledge and Data Engineering, Vol 21, No 10, 1475-1488, 2009.(Impact Factor: 2.285)
Bo Chen, Hongwei Liu and Zheng Bao. Optimizing the Data-dependent Kernel under a Unified Kernel Optimization framework. Pattern Recognition, 41(6), 2107-2119, 2008.(Impact Factor: 3.172)
Bo Chen, Hongwei Liu and Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher, Pattern Recognition, 41(3), 1098-1109, 2008.(Impact Factor: 3.172)
Bo Chen, Hongwei Liu, Li Yuan and Zheng Bao. Adaptively Segmenting Angular Sectors for Radar HRRP ATR. EURASIP Journal on Applied Signal Processing, Volume 2008 (2008), Article ID 641709, 6 pages.(Impact Factor: 1.055)
Bo Chen, Hongwei Liu and Zheng Bao. An Efficient Kernel Optimization Method for Radar High-resolution Range Profile Recognition, EURASIP Journal on Applied Signal Processing. Vol. 2007, Article ID 49597, 10 pages, 2007.(Impact Factor: 1.055)
Bo Chen, Li Yuan, Hongwei Liu and Zheng Bao. Kernel Subclass Discriminant Analysis. Neurocomputing, 71, 455-458, 2007.(Impact Factor: 1.595)
Bo Chen, Hongwei Liu and Zheng Bao. Basis Vector Classifier, Special issue of Dynamics of Continuous, Discrete and Impulsive Systems, 14 (s3), 23-29, 2007.
Bo Chen, Hongwei Liu, Zheng Bao. A Fusion Kernel Optimization Method. Journal of Xidian University, 34 (4), 509-513, 2007. (in Chinese)
Bo Chen, Hongwei Liu, Zheng Bao. Speeding up SVM in Test Phase: Application to Radar HRRP ATR. The Proc. of ICONIP’06, Hongkong, China, Vol.4232, 811-818, 2006, Springer Berlin.
Bo Chen, Hongwei Liu and Zheng Bao, An Efficient Kernel Optimization Method for High Range Resolution Profile Recognition, 2006 CIE International Conference on Radar, Shanghai, 1-4, 2006.
Bo Chen, Hongwei Liu and Zheng Bao, General Kernel Optimization Model Based on Kernel Fisher Criterion, ICNC2006, Xi’an, 143-146, Springer Berlin, 2006.
Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher Criterion, ISNN2006, Chengdu, 915-921, Springer Berlin, 2006.
Bo Chen, Hongwei Liu, Zheng Bao. PCA and Kernel PCA for Radar High Range Resolution Profiles Recognition. 2005 IEEE International Radar Conference in Arlington, Virginia USA: pp.528-533.
Bo Chen, Hongwei Liu, Zheng Bao and Xuefei Cao. A Kernel Optimization Algorithm Based on Fusion Kernel for High Range Resolution Profiles Recognition. ACTA ELECTRONICA SINICA, 34 (6), 1146-1151, 2006. (in Chinese)
Bo Chen, Hongwei Liu and Zheng Bao. Analysis and Comparison of Three Kinds of Classification Based Different Absolute Alignment Methods. Modern Radar, Vol. 28 (3), 58-62, 2006. (in Chinese)
Bo Chen, Hongwei Liu and Zheng Bao. An HRRP Recognition Method Based on Zero Phase Representation. Journal of Xidian University, 32 (5), 657-662, 2005. (in Chinese)
专利：
Raymond Langley, Stephen Kingsmore, Bo Chen and Lawrence Carin, Method for diagnosis of sepsis and risk of death, Pub. Number: US 2010/**, Pub. date: 10.28,2010.

---

荣誉获奖
陈渤教授成果：
Program Committee member for NIPS 2017
2013 新世纪优秀人才支持计划
2011 NIPS Travel Grant, Granada, Spain
2010年全国百篇优秀博士论文提名奖
2010年陕西省科技进步三等奖
2009年陕西省优秀博士论文奖
2009年西安电子科技大学优秀博士论文奖
2008 Travel Award for Summer Program in RIKEN BSI, Japan. (Every year only lessthan 45 ones are selected from the global applicants)
2008 Candidate for Marquis' Who's Who in the World 2009
2006 ICONIP Student Travel Award, ICONIP conference, Hongkong。

---

学生团队

---

学生生活：
1、学习-参加会议、比赛、举办会议
2、学习-暑期学校
3.生活-每年大组聚餐
4、生活-出差、参会间隙

学生毕业情况
博士：
王超杰（毕业时间：2021，工作：Nanyang Technological University博士后）
郭丹丹（毕业时间：2020，工作：Texas A&M University博士后）
王正珏（毕业时间：2020，工作：Cornell University博士后）
万锦伟（毕业时间：2020，工作：中电南湖研究院）
杨阳（毕业时间：2020，工作：解放军信息工程大学讲师）
张昊（毕业时间：2019，工作：Duke University、Cornell University博士后）
杜川（毕业时间：2019，工作：中山大学博士后）
徐彬（毕业时间：2019，工作：空军工程大学讲师）
丛玉来（毕业时间：2018，工作：Duke University博士后）
文伟（毕业时间：2018，工作：航天504所）
蒲文强（毕业时间：2018，工作：香港中文大学（深圳）博士后）
张学峰（毕业时间：2017，工作：航天二院23所）
冯博（毕业时间：2017，工作：航天二院23所）
丁军（毕业时间：2017，工作：西电信息感知集成攻关研究院副研究员）
硕士
武嘉文（毕业时间：2021，工作：字节跳动上海）
徐铭晟（毕业时间：2021，工作：微软苏州）
彭杨（毕业时间：2021，工作：寒武纪西安）
贾颖（毕业时间：2021，工作：工商银行西安）
刘家麒（毕业时间：2020，工作：字节跳动北京）
肖肃诚（毕业时间：2020，工作：字节跳动深圳）
袁以军（毕业时间：2020，工作：华为西安）
刘应琪（毕业时间：2020，工作：华为西安）
刘莹（毕业时间：2020，工作：华为西安）
赵倩茹（毕业时间：2020，工作：工商银行西安）
张志斌（毕业时间：2020，工作：中电41所青岛）
张梦娇（毕业时间：2019，工作：Stevens Institute of Technology博士）
李婉萍（毕业时间：2020，工作：中电14所南京）
周翼（毕业时间：2019，工作：高德红外武汉）
汪斌（毕业时间：2019，工作：全志科技西安）
沈梦启（毕业时间：2019，工作：阿里巴巴成都）
李伟一（毕业时间：2019，工作：宜信公司北京）
刘宁（毕业时间：2018，工作：通广龙电子科技北京）
鲍志业（毕业时间：2018，工作：浙江大华杭州）
李晨阳（毕业时间：2018，工作：招商银行郑州）
翟颖（毕业时间：2018，工作：中电20所西安）
肖定坤（毕业时间：2017，工作：腾讯深圳）
黄孟缘（毕业时间：2017，工作：华为东莞）
丁艳华（毕业时间：2017，工作：华为深圳）
刘名贵（毕业时间：2017，工作：联通云数据有限公司北京）
霍帅（毕业时间：2017，工作：全志科技西安）
李千勇（毕业时间：2017，工作：比智科技杭州）
饶辉（毕业时间：2016，工作：海尔集团青岛）
陈步华（毕业时间：2016，工作：中国电信研究院广州）

---

课程教学
目前本人承担的教学任务：
电院毕德显班和卓越班：模式识别与机器学习
电院卓越班：工程项目探讨
电院本科生：专业教育
国际教育学院留学生：Probability
研究生：特征学习

---

招生要求
1、每年将招收2名博士，欢迎highly motivated的学生报考。对于研究生推免生，优先考虑有硕博连读志向的申请者。
2、踏实、认真、严谨，对工作与学习有较强的积极主动性；
3、在编程、数学、信号处理等任一方面有较深的功底和较浓的兴趣；
4、本科生需要在“学有余力”的情况下，对科研有兴趣，并希望继续深造；
5、博士后需要在机器学习、信号处理等领域的权威期刊和会议发表过相关文章。
具有较强编程经验的考研学生将被优先考虑，欢迎报考本组研究生。
如果您有兴趣加入我的团队，无论本科生、研究生还是博士后，都请与我联系（邮件）并附上您的简历。我会尽快回复您。

---

Profile
Name Title: Dr. Bo Chen, Professor
Department: School of Electronic Engineering, Xidian University

Contact Information
Address:National Lab ofRadar Signal Processing, Xidian University,No 2 South Taibai Road, Xi\\\\\\\\\\\\\\\\\\\\'an, Shaanxi 710071, P. R. China
Room: 411 Building 100
Email: bchen@mail.xidian.edu.cn
Tel:


Introduction
1999.9-2003.7 B.S. at EE Department, Xidian University, Xi’an, Shaanxi, China
2003.9-2008.6 PhD in National Lab. of Radar Signal Processing, at EE Department,Xidian University
2008.11-2013.1 Research Scientist,SeniorResearch Scientistat ECE Department,Duke University,USA
2013.2-present Professor at School of Electronic Engineering, Xidian University, Xi\\\'an, Shaanxi, China
2014 Oversea Talent by Chinese Central Government
2019.7-present Associate Editor, IEEE Transactions on Signal Processing


News
Dandan and Meixi haveone paper,SAR Automatic Target Recognition based on Supervised Deep Variational Auto-encoding Model, accepted byIEEE Transactions on Aerospace and Electronic Systems, which takes advantage of deep probabilistic model to improve the explainability of SAR Target recognition task. Congratulations!
Zhibin and Dongsheng have one paper, Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network, accpeted by ICML 2021, which makes deep probabilistic model deeper and semantic distance between neurons from different layers measureble.Congratulations!
Zhibin and Hao have one paper, EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering, accepted by ACL 2021.Congratulations!
Ziheng Cheng has one paper, Memory-Efficient Network for Large-scale Video Compressive Sensing, accepted by IEEE CVPR2021.Congratulations!
Zhengjue Wang and Hao Zhang has one paper, MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing, accepted by IEEE CVPR2021.Congratulations!
Wenchao Chen has one paper,Tensor RNN with Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition,accepted by IEEE Transactions on Signal Processing.Congratulations!
Young Science and Technology Pacesetter of Shaanxi province in 2021.
Wenchao Chen has one paper,Max-Margin Deep Diverse Latent Dirichlet Allocation with continual learning,accepted by IEEE Transactions on Cybernetics.Congratulations!
Dandan Guo has one paper,Variational Temporal Deep Generative Model for Radar HRRP Target Recognition, accepted by IEEE Transactonson Signal Processing, which employs our dynamic deep generative model for radar HRRP.
Zhengjue has one paper, Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational Probabilistic Generative Model, accepted byIEEE Trans. on Neural Networks and Learning Systems.
Zhengjue and Zhibin Duan have one paper, Friendly Topic Assistant for Transformer Based Abstractive Summarization, accepted bythe 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP2020.
Our 3rdSIILP2020 (International Workshop on Signal and Information Intelligent Learning and Processing) will be held online in Tencent Meeting and VooV meeting in 15-16 August, 2020.
Wei Wen, Xuefeng Zhang and Zhengjue Wang have one paper, Infinite Bayesian Max-Margin Discriminant Projection accepted by IEEE Transactions on Cybernetics.Congratulations!
Ruiying Lu and Ziheng Zhang have one paper, RAFnet: Recurrent Attention Fusion Network of Hyperspectral and Multispectral images, accepted by Signal Processing.Congratulations!
Ziheng Cheng and Ruiying Lu has one paper, BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging, accepted byECCV2020. Congratulations! The work produces the state of the art results on synthetic and real benchmark data for video Snapshot Compressive Imagin (SCI) task.
Hao Zhang has one paper，Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference，accepted by IEEE Trans. on Pattern Analysis and Machine Intelligence. Congratulations!
Zhengjue Wang and Ruiying Lu have one paper, FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion, accepted by IEEE Trans. on Image Processing.Congratulations!
Dandan Guo and Ruiying Lu have one paper,Recurrent Hierarchical Topic-Guided RNN for Language Generation, accepted by ICML2020.Congratulations!
Wenchao and Yicheng, an undergraduate intern in my Lab, have one paper, Switching Poisson Gamma Dynamical System, accepted by IJCAI2020. Long and Yang have one paper, META NETWORK FOR RADAR HRRP NONCOOPERATIVE TARGET RECOGNITION WITH MISSING ASPECTS, accepted by IGARSS2020 as an oral. Congratulations!
Hao Zhang and Long Tian have one paper，Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling,accepted by ICLR.Congratulations!
FromJuly, I will be an Associate Editor for IEEETransactions on SignalProcessing.
Chaojie Wang and Sucheng Xiao have one paper，Convolutional Poisson Gamma Belief Network, accepted by ICML2019.Congratulations! An end-to-end probabilistic convolutional autoencoder, can handle the one-hot data of word order without word embedding as preprocessing. Code has been released!
Three papers accepted by Signal Processing.
The code about, WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling, has been released！
Dandan Guo and Hao Zhang have one paper，Deep Poisson Gamma Dynamic Systems, accepted by NIPS2018.Congratulations!
Hao has one paper, DeepMax-Margin Discriminant Projection, accepted by IEEE Transactions on Cybernetics. Congratulations!
Hao and Dandan have one paper, WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling, Accpeted by the Sixth International Conference on Learning Representations (ICLR2018). Congratulations! A deep probabilistic autoencoder model with the high efficiency of parameter learning like conventional deepnetworks and goodinterpretable and uncertain property like probabilistic modeling.
Chaojie has one paper, Multimodal Poisson Gamma Belief Network, Accpeted by the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Congratulations!
Zhengjue Wang and Ying Zhai received the National scholarship. Congratulations!
Program Committee memberfor NIPS 2017
Yulai had one paper, Deep Latent Dirichlet Allocation with Topic-Layer-AdaptiveStochastic Gradient Riemannian (TLASGR) MCMC, Accpeted by International Coference on Machine Learning (ICML) 2017. Congratulations! A stochasticonline deep probabilistic generative model.
Yulai had one paper, Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions,accepted by Bayesian Analysis. Congratulations!
2016 Electronic and Information Engineering Symposiam (EIES2016) will be held during 11.12-11.13 in Xidian Unviersity by our National Lab of Radar Signal Processing, which is free for registration.http://meeting.xidian.edu.cn/symposium/eies2016/
Our team won the second place in the competition of Radar Target Recognition. Congs Hao Zhang, Jinwei Wan, Chaojie Wang and Long Tian!
Bo Feng had one paper accepted by Pattern Recognition, although going through the review procdure fortwo years. Congratulations!
Wei Wen had one paper accepted by Information Sciences. Congratulations!
Yulai had one paper accepted by IEEE trans. on Signal Processing. Congratulations!
Xuefeng and Jun had papers accepted by Pattern Recognition and GRSL. Congratulations!
Visited Montreal for NIPS2015.
Talks at the workshop on advanced radar technique.
Talks at Nanjing University of Information Science & Techonology and Radar XueBao.
Hao Zhang and Yanhua Ding received the National scholarship. Congratulations!
Mengyuan and Dingkun received the 1st class scholarship. Congs!
One paper working with Prof. Zhou from UTAustin, The Poisson Gamma Belief Network, has been accepted by NIPS2015.
I received the advanced research key foundation from PLA General armament Department
Bo Feng successfully finished his PhD defense. Congratulations!
I co-organized MIIS2015, which will be held in Beijing next year.
I gave a talk at MLA2015.
I co-organized MIIS2014.
I joined VALSE2014 Panel discussion.
I was selected by One Thousand Young Talent Program in 2014.
I was selected by New Century Excerlent Talent Program in 2013
I received one NSFC grant.

Research Interests
Statistical Signal and information processing
Statistical machine learning (Bayesian models and inference)
Online inference
Deep learning and its application for large-scale image analysis using GPUs
Pattern recognition (radar automatic target recognition)

---

Research
目前研究团队承担的科研项目：

---

Selected Publications
Dandan Guo, Bo Chen*, Meixi Zheng andHongwei Liu, SAR Automatic Target Recognition based on Supervised Deep Variational Auto-encoding Model，to appear in IEEE Transactions on Aerospace and Electronic Systems, 2021.
Zhibin Duan, Dongsheng Wang, Bo Chen*, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren and Mingyuan Zhou, Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network,to appear inInternational Conference on Machine Learning (ICML) 2021.
Shujian Zhang, Xinjie Fan, Bo Chen and Mingyuan Zhou,Bayesian Attention Belief Networks, to appear inInternational Conference on Machine Learning (ICML) 2021.
Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang,Bo Chen*, and Mingyuan Zhou:EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering, to appear in the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) Bangkok, Thailand, 1-6 August 2021.
Chaojie Wang, Bo Chen*,Sucheng Xiao, Zhengjue Wang, Hao Zhang, Penghui Wang, Ning Han, and Mingyuan Zhou,Multimodal Weibull Variational Autoencoder for Jointly Modeling Image-Text Data,accepted by IEEE Transactions on Cybernetics, 2021.
Wenchao Chen, Bo Chen*,Xiaojun Peng, Jiaqi Liu, Yang Yang, Hao Zhang and Hongwei Liu,Tensor RNN with Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition，to appear in Transactions on Signal Processing, 2021.
ZihengCheng, BoChen*, GuanliangLiu, HaoZhang, RuiyingLu, ZhengjueWang and X. Yuan*, Memory-Efficient Network for Large-scale Video Compressive Sensing, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
ZhengjueWang, HaoZhang, ZihengCheng, BoChen* and XinYuan*, MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing,IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Wenchao Chen, Xuefei Cao, Bo Chen*,Yingqi Liu, Qianru Zhao, Hao Zhang,Max-Margin Deep Diverse Latent Dirichlet Allocation with continual learning,accepted by IEEE Transactions on Cybernetics, 2021.
Wenchao Chen,Chaojie Wang, Bo Chen*, Yicheng Liu, Hao Zhangand Mingyuan Zhou,Bidirectional Convolutional Poisson Gamma Dynamical Systems,to appear inAdvances in Neural Information Processing Systems (NeurIPS), Virtual,2020. [Code]
Chaojie Wang, Hao Zhang, Bo Chen*, Dongsheng Wang, Zhengjue Wang and Mingyuan Zhou,Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network,to appear inAdvances in Neural Information Processing Systems (NeurIPS), Virtual,2020. [Code]
Xinjie Fan, Shujian Zhang, Bo Chen and Mingyuan Zhou, Bayesian Attention Modules,to appear inAdvances in Neural Information Processing Systems (NeurIPS), Virtual,2020.
Dandan Guo, Bo Chen*, Wenchao Chen, Chaojie Wang, Hongwei Liu, and Mingyuan Zhou,Variational Temporal Deep Generative Model for Radar HRRP Target Recognition, to appear inIEEE Transactonson Signal Processing, 2020.
Zhengjue Wang, Bo Chen*, Hao Zhang, and Hongwei Liu, Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational Probabilistic Generative Model, to appear in IEEE Transactions on Neural Networks and Learning Systems 2020.
Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen*and Mingyuan Zhou, Friendly Topic Assistant for Transformer Based Abstractive Summarization, to appear in the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP2020, Dominican Republic, November, 2020. [Code]
Wei Wen, Bo Chen*, Xuefei Cao*, Xuefeng Zhang, Zhengjue Wang and Hongwei Liu, Infinite Bayesian Max-Margin Discriminant Projection, to appear in IEEE Transactions on Cybernetics 2020.
Ruiying Lu, Bo Chen*, Ziheng Cheng andPenghui Wang*, RAFnet: Recurrent Attention Fusion Network of Hyperspectral and Multispectral images, to appear in Signal Processing 2020.
Ziheng Cheng, Ruiying Lu, Zhengjue Wang, Hao Zhang, Bo Chen*, Ziyi Meng and Xin Yuan*,BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging, to appear in European Conference on Computer Vision (ECCV), Glasgow, August 2020. [Code]
Hao Zhang, Bo Chen*, Yulai Cong, Dandan Guo, Hongwei Liu, and Mingyuan Zhou，Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference，to appear in IEEE Trans. on Pattern Analysis and Machine Intelligence.
Zhengjue Wang, Bo Chen*, Ruiying Lu, Hao Zhang, Hongwei Liu, and Pramod K. Varshney, FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion, IEEE Trans. on Image Processing, Vol.29. 7565-7577, 2020.
Dandan Guo,Bo Chen*, Ruiying Lu and Mingyuan Zhou,Recurrent Hierarchical Topic-Guided RNN for Language Generation, to appear in International Conference on Machine Learning (ICML), Vienna, Austria, July 2020.
Wenchao Chen , Bo Chen*, Yicheng Liu, Qianru Zhao, Mingyuan Zhou, Switching Poisson Gamma Dynamical Systems, to appear in the 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, July, 2020.
Zhengjue Wang, Chaojie Wang, HaoZhang, ZhibinDuan, MingyuanZhou, and BoChen*, Learning dynamic hierarchical topic graph with graph convolutional network for document classification,International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy, June 2020.
Hao Zhang, Bo Chen*,Long Tian, Zhengjue Wang and Mingyuan Zhou,Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling,in International Conference on Learning Representations (ICLR), Addis Ababa ETHIOPIA, May 2020. [Code]
Chaojie Wang, Bo Chen*, Sucheng Xiao, and Mingyuan Zhou, Convolutional Poisson Gamma Belief Network, to appear in International Conference on Machine Learning (ICML), Long Beach, CA, USA, June2019. [Code]
Jinwei Wan, Bo Chen*,Bin Xu, Hongwei Liu and Lin Jin, Convolutional Neural Networks for Radar HRRP Target Recognition and Rejection,EURASIP, 2019(1):5,2019.
Chuan Du, Bo Chen*,Hongwei Liuand Bin Xu, Factorized Discriminative Conditional Variational Auto-encoder for Radar HRRP Target Recognition,Signal Processing, 158,176-189,2019.
Zhengjue Wang,Bo Chen*, Hao Zhang and Hongwei Liu, Variational Probabilistic Generative Framework for Single Image Super-Resolution, Signal Processing, 156, 92-105, 2019.
Bin Xu,Bo Chen*, Jinwei Wan, Hongwei Liu, and Lin Jin, Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition, Signal Processing, 155, 268-280, 2019.
Dandan Guo,Bo Chen*,Hao Zhang,and Mingyuan Zhou,Deep Poisson Gamma Dynamic Systems, to appear inAdvances in Neural Information Processing Systems (NIPS), 2018, Montreal, Canada.
Hao Zhang,Bo Chen*, Zhengjue Wang, and Hongwei Liu, Deep Max-Margin Discriminant Projection, IEEE Transactions on Cybernetics, Vol. 49, No. 7, 2454-2466, July2019.
Hao Zhang,Bo Chen*, Dandan Guo, and Mingyuan Zhou, WHAI: Weibull hybrid autoencoding inference for deep topic modeling, to appear in International Conference on Learning Representations (ICLR), Vancouver, Canada, May 2018. [Code]
Chaojie Wang,Bo Chen*, Hongwei Liu, and Mingyuan Zhou, Multimodal Poisson Gamma Belief Network, to appear in the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Lousiana, USA, Feburary 2018. [Code]
Yulai Cong,Bo Chen*, Hongwei Liu, and Mingyuan Zhou, Deep latent Dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC, to appear in International Conference on Machine Learning (ICML), Sydney, Australia, August 2017.
Yulai Cong,Bo Chen*, and Mingyuan Zhou, Fast simulation of hyperplane-truncated multivariate normal distributions, Bayesian Analysis, Vol. 12, No. 4, pp: 1017-1037, 2017. [Code]
Bo Feng,Bo Chen*and Hongwei Liu,Radar HRRP target recognition with deep networks,Pattern Recognition 61, 379-393, 2017.
Jing Chai, Bo Chen, Fan Liu, Zehua Chen, Xinghao Ding. Multiple-Instance Feature Extraction at the Bag and Instance Levels Using the Maximum-Trace Difference Criterion. Information Sciences: 2017 ,385-386 ,353-377.
Mingyuan Zhou*, Yulai Cong, andBo Chen*,Augmentable gamma belief networks, Journal of Machine Learning Research,17(163), 1-44, 2016.
Yulai Cong,Bo Chen*, Hongwei Liu, Bo Jiu. Nonparametric Bayesian Attributed Scattering Center Extraction for Synthetic Aperture Radar Targets. IEEE Transactions on Signal Processing, 2016. Accepted for publication.
Jun Ding,Bo Chen*, Hongwei Liu, Mengyuan Huang, Convolutional Neural Network With Data Augmentation for SAR Target Recognition, IEEE Geoscience and Remote Sensing Letters, 13(3), 364-368, 2016.
Xuefeng Zhang,Bo Chen*, Hongwei Liu, Lei Zuo, and Bo Feng, Infinite max-margin factor analysis via data augmentation. Pattern Recognition 52, 17-32, 2016.
Hongwei Liu, Bo Feng,Bo Chen*, Lan Du, Radar high-resolution range profiles target recognition based on stable dictionary learning, IET Radar, Sonar & Navigation, Vol. 10, Iss. 2, pp. 228–237, 2016.
Mingyuan Zhou, Yulai Cong,Bo Chen, The Poisson Gamma Belief Network, Advances in Neural Information Processing Systems (NIPS). 3025-3033, 2015, Montreal, Canada.
Junkun Yan, Hongwei Liu, Bo Jiu,Bo Chen, Zheng Liu, Zheng Bao:Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking. IEEE Transactions on Signal Processing, 63(12): 3110-3122, 2015. (Impact Factor: 3.2)
Bo Chen, Hao Zhang, Xuefeng Zhang, Wei Wen, Hongwei Liu and Jun Liu, Max-Margin Discriminant Projection via Data Augmentation, IEEE Transactions on Knowledge and Data Engineering,27(7), 1964-1976, 2015.(Impact Factor: 1.8)
Bo Jiu, Hongwei Liu, Xu Wang, Lei Zhang, Yinghua Wang,Bo Chen, Knowledge-Based Spatial-Temporal Hierarchical MIMO Radar Waveform Design Method for Target Detection in Heterogeneous Clutter Zone, IEEE Transactions on Signal Processing, 63(3), 543-554, 2015.(Impact Factor: 3.2)
Junkun Yan, Bo Jiu, Hongwei Liu,Bo Chen, Zheng Bao: Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter. IEEE Transactions on Signal Processing, 63(2): 512-527, 2015.(Impact Factor: 3.2)
Raymond J. Langley, Ephraim L. Tsalik, Jennifer C. vanVelkinburgh, Seth W. Glickman, Brandon J. Rice, Chunping Wang,BoChen, Lawrence Carin, Arturo Suarez, Robert P. Mohney, Debra H.Freeman, Mu Wang, Jinsam You, Jacob Wulff, J. Will Thompson, M. ArthurMoseley, Stephanie Reisinger, Brian T. Edmonds, Brian Grinnell, DavidR. Nelson, Darrell L. Dinwiddie, Neil A. Miller, Carol J. Saunders,Sarah S. Soden, Angela J. Rogers, Lee Gazourian , Laura E. Fredenburgh,Anthony F. Massaro, Rebecca M. Baron, Augustine M.K. Choi, G. RalphCorey, Geoffrey S. Ginsburg, Charles B. Cairns, Ronny M. Otero, VanceG. Fowler Jr, Emanuel P. Rivers, Christopher W. Woods, Stephen F.Kingsmore, An integrated clinico-me[ant]tabolomic model improves prediction of death in sepsis, Science Translational Medicine, Vol. 5, Issue 195, p. 195ra95, July, 2013.(Impact Factor: 10.8)
Bo Jiu, Hongwei Liu,Bo Chen, Zheng Liu, Waveform Design for Wideband Radar Target Recognition Based on Eigensubspace Projection, IET Radar, Sonar & Navigation,7(6), 702–709, 2013.(Impact Factor: 1.0)
Bo Chen, Gungor Polatkan, Guillermo Sapiro, David Blei, David Dunson and Lawrence Carin, Deep Learning with Hierarchical Convolutional Factor Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence,35(8), 1887-1901, 2013.(Impact Factor: 4.908)
Bo Chen, David E. Carlson and Lawrence Carin, On the Analysis of Multi-Channel Neural Spike Data, Neural Information Processing Systems (NIPS) 2011, Granada, Spain.
Bo Chen, Guillermo Sapiro, Gungor Polatkan, David Dunson and Lawrence Carin, The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning, International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA. [online video: http://techtalks.tv/talks/54368/]
Bo Chen, Guillermo Sapiro, David Dunson, Lawrence Carin, Deep Networks with Hierarchical Convolutional Factor Analysis, Neural Information Processing Systems (NIPS) 2010 Workshop on Deep Learning and Unsupervised Feature Learning, Vancouver, Canada.
Bo Chen, Minhua Chen, John Paisley, Aimee Zaas, Christopher Woods, Geoff Ginsburg, Alfred Hero III, Joseph Lucas, David Dunson and Lawrence Carin. Bayesian inference of the number of factors in gene-ex[ant]pression analysis: application to human virus challenge studies. BMC Bioinformatics, Volume 11, Number 1, 552, 2010.(Impact Factor: 3.028)
Bo Chen, John Paisley and Lawrence Carin. Sparse Linear Regression with Beta Process Priors, ICASSP 2010, Dallas, USA.
Jing Chai, Hongwei Liu,Bo Chenand Zheng Bao. Large margin nearest local mean classifier. Signal Processing, Volume 90 Issue 1, January, 2010.(Impact Factor: 1.503)
Bo Chen, Hongwei Liu, Jing Chai and Zheng Bao. Large Margin Feature Weighting via Linear Programming. IEEE Transactions on Knowledge and Data Engineering, Vol 21, No 10, 1475-1488, 2009.(Impact Factor: 2.285)
Bo Chen, Hongwei Liu and Zheng Bao. Optimizing the Data-dependent Kernel under a Unified Kernel Optimization fr[ant]amework. Pattern Recognition, 41(6), 2107-2119, 2008.(Impact Factor: 3.172)
Bo Chen, Hongwei Liu and Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher, Pattern Recognition, 41(3), 1098-1109, 2008.(Impact Factor: 3.172)
Bo Chen, Hongwei Liu, Li Yuan and Zheng Bao. Adaptively Segmenting Angular Sectors for Radar HRRP ATR. EURASIP Journal on Applied Signal Processing, Volume 2008 (2008), Article ID 641709, 6 pages.(Impact Factor: 1.055)
Bo Chen, Hongwei Liu and Zheng Bao. An Efficient Kernel Optimization Method for Radar High-resolution Range Profile Recognition, EURASIP Journal on Applied Signal Processing. Vol. 2007, Article ID 49597, 10 pages, 2007.(Impact Factor: 1.055)
Bo Chen, Li Yuan, Hongwei Liu and Zheng Bao. Kernel Subclass Discriminant Analysis. Neurocomputing, 71, 455-458, 2007.(Impact Factor: 1.595)
Bo Chen, Hongwei Liu and Zheng Bao. Basis Vector Classifier, Special issue of Dynamics of Continuous, Discrete and Impulsive Systems, 14 (s3), 23-29, 2007.
Bo Chen, Hongwei Liu, Zheng Bao. A Fusion Kernel Optimization Method. Journal of Xidian University, 34 (4), 509-513, 2007. (in Chinese)
Bo Chen, Hongwei Liu, Zheng Bao. Speeding up SVM in Test Phase: Application to Radar HRRP ATR. The Proc. of ICONIP’06, Hongkong, China, Vol.4232, 811-818, 2006, Springer Berlin.
Bo Chen, Hongwei Liu and Zheng Bao, An Efficient Kernel Optimization Method for High Range Resolution Profile Recognition, 2006 CIE International Conference on Radar, Shanghai, 1-4, 2006.
Bo Chen, Hongwei Liu and Zheng Bao, General Kernel Optimization Model Based on Kernel Fisher Criterion, ICNC2006, Xi’an, 143-146, Springer Berlin, 2006.
Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher Criterion, ISNN2006, Chengdu, 915-921, Springer Berlin, 2006.
Bo Chen, Hongwei Liu, Zheng Bao. PCA and Kernel PCA for Radar High Range Resolution Profiles Recognition. 2005 IEEE International Radar Conference in Arlington, Virginia USA: pp.528-533.
Bo Chen, Hongwei Liu, Zheng Bao and Xuefei Cao. A Kernel Optimization Algorithm Based on Fusion Kernel for High Range Resolution Profiles Recognition. ACTA ELECTRONICA SINICA, 34 (6), 1146-1151, 2006. (in Chinese)
Bo Chen, Hongwei Liu and Zheng Bao. Analysis and Comparison of Three Kinds of Classification Based Different Absolute Alignment Methods. Modern Radar, Vol. 28 (3), 58-62, 2006. (in Chinese)
Bo Chen, Hongwei Liu and Zheng Bao. An HRRP Recognition Method Based on Zero Phase Representation. Journal of Xidian University, 32 (5), 657-662, 2005. (in Chinese)
Patents:
Raymond Langley, Stephen Kingsmore,Bo Chenand Lawrence Carin, Method for diagnosis of sepsis and risk of death, Pub. Number: US 2010/**, Pub. date: 10.28,2010.

---

Honors
Program Committee member for NIPS 2017
2011 NIPS Travel Grant, Granada, Spain
2010 Honorable Mention for 2010 National Excellent Doctoral Dissertation Award (Highest Award for PhD Thesis in China)
2009 Advance of Science and Technology Second Prize, Shaanxi Province Education Department
2009 Shaanxi Province Excellent PhD Thesis
2009 Xidian University Excellent PhD Thesis
2008 Travel Award for Summer Program in RIKEN BSI, Japan. (Every year only less than 45 ones are selected from the global applicants)
2008 Candidate for Marquis' Who's Who in the World 2009
2006 ICONIP Student Travel Award, ICONIP conference, Hongkong

---

Team
Students：16postgraduate students ；13doctoral students

---

Teaching
目前本人承担的教学任务：

课件下载 示例

---

Admission
If you are interested in any of our research fields and would like to work with me, you are encouraged to send me an email with your CV attached. All levels of students (undergraduate, graduate and post-docs) are welcomed to join us. I will get back to you as soon as possible.