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  • Control Science and Engineering
  • 宇航学院
 

Education Experience

Job Experience

 

Introduction

Dr. Yue Deng is a full professor in Beihang University. Before that, he was a postdoctoral researcher in Altschuler&Wu lab in UCSF. His research interests mainly focused on Artificial Intelligence and their applications for interdisciplinary sciences. He has authored more than 40 papers on leading journals including Nature Methods and several IEEE Transactions. His work on hierarchical fused deep learning wins the 2020 outstanding paper award of IEEE Transactions on Fuzzy Systems. He is a Microsoft research fellow and serves as PC members for many top AI conferences including ICCV, IJCAI and AAAI.

 

Our lab has some openings for postdocs who want to conduct AI research for interdisciplinary sciences.

 

Selected Publications

 

AI for computational biology

 

Yue Deng, Feng Bao, Steven Altschuler, Lani Wu: Scalable Analysis of Cell-type Composition from Single-cell Transcriptomics using Deep Recurrent Learning, Nature Methods, 16(4).March 2019

Yue Deng, Feng Bao, Mulong Du, Meilin Wang, Qionghai Dai: Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification. Nucleic acids research, 45(15), e143-e143 (2017).

Yue Deng, Feng Bao, Mulong Du, Zhiquan Ren, Qingzhao Zhang, Yanyu Zhao, Jinli Suo, Zhengdong Zhang, Meilin Wang, and Qionghai Dai.Probabilistic natural mapping of gene-level tests for genome-wide association studies. Briefings in bioinformatics 19(4) 545-553 (2017).

Yue Deng, Steven Altschuler, Lani Wu: PHOCOS: Inferring Multi-Feature Phenotypic Crosstalk Networks. Bioinformatics. 2016 Jun 15; 32(12):i44-i51.

 

 

AI technology

Yue Deng, Zhiquan Ren, Youyong Kong, Feng Bao, Qionghai Dai. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification. IEEE Trans. on Fuzzy Systems. 25(4), 1006-1012 (2017) (IEEE Transactions on Fuzzy Systems 2020Outstanding Paper Award)

Yue Deng, Qionghai Dai, Risheng Liu, Zengke Zhang, Sanqing Hu Low-Rank Structure Learning via Nonconvex Heuristic Recovery. IEEE Trans. on Neural Networks and Learning Systems 24(3): 383-396 (2013) (IEEE computational intelligence society spotlight paper)

Yue Deng, Feng Bao, Zhiquan Ren, Youyong Kong, Qionghai Dai. Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Trans. on Neural Networks and Learning Systems. 28(3):653-664 (2017). (IEEE TNNLS Popular Articles)

Yue Deng, Youyong Kong, Feng Bao, and Qionghai Dai: Sparse coding-inspired optimal trading system for HFT industry. IEEE Trans. on Industrial Informatics 11(2), 467-475 (2015).

Yue Deng, Feng Bao, Xuesong Deng, Ruiping Wang, Youyong Kong and Qionghai Dai:Deep and Structured Robust Information Theoretic Learning for Image Analysis. IEEE Trans on Image Processing 25(9): 4209-4221 (2016)

Yue Deng, Yipeng Li, Yanjun Qian, Xiangyang Ji, Qionghai Dai: Visual Words Assignment via Information-Theoretic Manifold Embedding. IEEE Trans. on Cybernetics 44(10): 1924-1937 (2014)

Yue Deng, Yanyu Zhao, Zhiquan Ren, Youyong Kong, Feng Bao and Qionghai Dai. Discriminant Kernel Assignment for Image Coding. IEEE Trans. on Cybernetics. 47(6), 1434-1445 (2017)  

Yue Deng, Yebin Liu, Qionghai Dai, Zengke Zhang, Yao Wang: Noisy Depth Maps Fusion for Multiview Stereo via Matrix Completion. IEEE Journal of Selected Topics in Signal Processing 6(5): 566-582 (2012)

Yue Deng, Qionghai Dai, Zengke Zhang: Graph Laplace for Occluded Face Completion and Recognition. IEEE Trans. on Image Processing 20(8): 2329-2338 (2011)

Yue Deng, Yilin Shen, Hongxia Jin: Adversarial Active Learning for Sequence Labeling and Generation. International Joint Conferences on Artificial Intelligence, 2018

Yue Deng, Yilin Shen, Hongxia Jin: Disguise Neural Networks for Click-through Rate Prediction. International Joint Conferences on Artificial Intelligence 2017

Yue Deng, Yilin Shen, Hongxia Jin: Learning Assistance from An Adversarial Critic for Multi-output Prediction, International Joint Conferences on Artificial Intelligence 2019

Tian Tan, Feng Bao, Yue Deng, Alex Jin, and Qionghai Dai: Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control. IEEE Trans. on Cybernetics (2019).

Feng Bao, Yue Deng, Zhiquan Ren, Youyong Kong, Qionghai Dai: Learning Deep Landmarks for Imbalanced Data Classification. IEEE Trans. on Neural Networks and Learning Systems (2019)

 

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