Dr. Yue Deng is a joint professor in the School of Astronautics and the Institute of Artificial Intelligence, Beihang University. His research interests mainly focused on Artificial Intelligence and their applications for interdisciplinary sciences. He has authored more than 50 papers on leading journals including Nature Biotechnology, Nature Methods, Patterns and several IEEE Transactions. His work won the IEEE Transactions on Fuzzy Systems 2020 Outstanding Paper Award and the Cell Press Most Picked Paper Award 2021. He is a Microsoft research fellow and served as Associate Editors for IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) and IEEE Signal Processing Letters (IEEE SPL).

Selected Publications

 （* indicates co-first authorship, # indicates corresponding authorship）

Feng Bao*, Yue Deng* et al.: Characterizing Tissue composition through Combined Analysis of Single-cell Morphologies and Transcriptional States, Nature Biotechnology, 2022 

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

Feng Bao*, Yue Deng*#  et al.:Explaining the genetic causality for complex phenotypes via deep association kernel learning, Patterns, 1 AUG 2020. (Cell Press Most Picked Paper Award).

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 2020 Outstanding 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).

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.

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)