XU, Linchuan (许林川)
Research Assistant Professor
Department of Computing
The Hong Kong Polytechnic University
PQ813, Mong Man Wai Building, PolyU
Hung Hom, Kowloon, Hong Kong SAR, China
E-mail: linch.xu (at) polyu.edu.hk
Tel: (852) 2766 7276
Fax: (852) 2774 0842
I am looking for research assistants (full-time/part-time) for deep learning related research. Please feel free to contact me with CV for more information.
[June 2022] Invited to visit The University of Tokyo from 11th July to 31th July.
[Apr 2022] Our paper entitled "Network Change Detection Based on Random Walk in Latent Space" has been accepted for publication in IEEE Transactions on Knowledge and Data Engineering.
[Mar 2022] Invited to be a Guest Editor of Frontiers in Big Data
[Jan-Feb 2022] Invited to PC members of ICML2022, ECCV2022, NeurIPS2022
I am currently a research assistant professor with Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. Prior to that, I was a post-doctoral researcher with Department of Mathematical Informatics, Graduate School of Information Science and Technology at the University of Tokyo, Japan, under the supervision of Professor Kenji Yamanishi, from August 2018 to June 2020. I received the B.E. degree in Information Engineering from Beijing University of Posts and Telecommunications in 2013, and the Ph.D. degree from Department of Computing of the Hong Kong Polytechnic University, under the supervision of Professor Jiannong Cao, in 2018. From 2015 to 2016, I visited BDSC lab led by Professor Philip S. Yu in University of Illinois at Chicago, USA.
Keywords: Big Data Analytics, Health Informatics, Bioinformatics, Network Analysis.
My research interest lies primarily in big data analytics with emphasis on health/medical applications and network applications. Real-world systems are complex systems, such as health systems and social systems. To understand how complex systems work and to spot misfunctions of complex systems, one of the most effective ways is to observe and analyze the data generated by complex systems. My research aims to develop computational methods to perform data analytical tasks. In particular, I design data mining and machine learning models to analyze big data that feature unstructured format, heterogeneous modalities, time-varying sequences, etc. Moreover, I incorporate domain knowledge about the complex system under study into the design of the models. I have demonstrated my research in many applications, such as estimating the severity of glaucoma, categorizing the participants of networks, inferring potential interactions among participants, and detecting novel events happening to the systems underlying networks.
2022
2021
Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, Chuan-hao Lin, Change Sign Detection with Differential MDL Change Statistics and Its Applications to COVID-19 Pandemic Analysis, Scientific Reports, 2021, 11(1), pp. 1-15.
Ryo Asaoka, Linchuan Xu, Hiroshi Murata, Taichi Kiwaki, Masato Matsuura, Yuri Fujino, Masaki Tanito, Kazuhiko Mori, Yoko Ikeda, Takashi Kanamoto, Kenji Inoue, Jukichi Yamagami, Kenji Yamanishi, A joint multitask learning model for cross-sectional and longitudinal predictions of visual field using optical coherence tomography, to appear in Ophthalmology Science Journal.
Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, Kenji Yamanishi, Multi-label learning with missing and completely unobserved labels, to appear in Data Mining and Knowledge Discovery.
Wei Li, Linchuan Xu, Zhixuan Liang, Senzhang Wang, Jiannong Cao, Thomas C.Lam, Xiaohui Cui, JDGAN: Enhancing generator on extremely limited data via joint distribution, Neurocomputing, 2021, 431, pp. 148-162.
Linchuan Xu, Ryo Asaoka, Hiroshi Murata, Taichi Kiwaki, Yuhui Zheng, Masato Matsuura, Yuri Fujino, Masaki Tanito, Kazuhiko Mori,Yoko Ikeda, Takashi Kanamoto, Kenji Yamanishi, Improving visual field trend analysis with optical coherence tomography and deeply-regularized latent-space linear regression, Ophthalmology Glaucoma, 4(1), pp. 78-88.
2020
Wei Li, Linchuan Xu, Zhixuan Liang, Senzhang Wang, Jiannong Cao, Chao Ma, Xiaohui Cui, Sketch-then-Edit Generative Adversarial Network, to appear in Knowledge-Based Systems.
Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroki Sugiura, Yohei Hashimoto, Shotaro Asano, Hiroshi Murata, Atsuya Miki, Kazuhiko Mori, Yoko Ikeda, Takashi Kanamoto, Junkichi Yamagami, Kenji Inoue, Masaki Tanito, Kenji Yamanishi, Predicting the Glaucomatous Central 10 Degrees Visual Field from Optical Coherence Tomography using Deep Learning and Tensor Regression, American Journal of Ophthalmology, 2020.
2019
Linchuan Xu, Jing Wang, Lifang He, Jiannong Cao, Xiaokai Wei, Philip S. Yu, Kenji Yamanishi, MixSp: A Framework for Embedding Heterogeneous Information Networks with Arbitrary Number of Node and Edge Types, IEEE Transactions on Knowledge and Data Engineering, 2019.
Linchuan Xu, Jiannong Cao, Xiaokai Wei, Philip S. Yu, Network Embedding via Coupled Kernelized Multi-dimensional Array Factorization, IEEE Transactions on Knowledge and Data Engineering, 2019.
2018
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, Multi-task Network Embedding, International Journal of Data Science and Analytics, 2018, 8(2), pp.183-198.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, ICANE: Interaction Content-Aware Network Embedding via Co-embedding of Nodes and Edges, International Journal of Data Science and Analytics, 2018. pp. 1-14.
2022
Zipei Yan, Linchuan Xu, Atsushi Suzuki, Jing Wang, Jiannong Cao, and Jun Huang, RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation, IEEE International Conference on Big Data, to appear.
Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, and Linchuan Xu. "Contrastive Knowledge Graph Error Detection." In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 2590-2599.
2021
Atsushi Suzuki, Atsushi Nitanda, jing wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza, Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic, to appear in NeurIPS 2021.
Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, and Kenji Yamanishi, PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma, to appear in KDD 2021.
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi and Marc Cavazza, Generalization Error Bound for Hyperbolic Ordinal Embedding, to appear in ICML 2021.
2020
2019
Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi, Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression, KDD 2019. August 3-7, 2019. Alaska, USA. pp. 2278-2286.
Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi, Attributed Subspace Clustering, IJCAI 2019. August 10-16, 2019. Macao, China. pp. 3719-3725.
Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi, Orderly Subspace Clustering, AAAI 2019. January 27 - February 1, 2019. Hawaii, USA. pp. 5264-5272.
2018
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, On Learning Community-specific Similarity Metrics for Cold-start Link Prediction, IJCNN 2018. July 8-13, 2018. Rio, Brazil
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, ICANE: Interaction Content Aware Network Embedding via Co-embedding of Nodes and Edges, PAKDD2018. June 3-6, 2018. Melbourne, Australia.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, On Exploring Semantic Meanings of Links for Embedding Social Networks, WWW 2018. April 23-27, 2018. Lyon, France. pp. 479-488.
2017
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, Multiple Social Role Embedding, DSAA 2017. October 19-21, 2017. Tokyo, Japan. pp. 581-589.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, Multi-task Network Embedding, DSAA 2017. October 19-21, 2017. Tokyo, Japan. pp. 571-580.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, Disentangled Link Prediction for Signed Networks via Disentangled Representation Learning ( Best Research Paper), DSAA 2017. October 19-21, 2017. Tokyo, Japan. pp. 676-685.
Xiaokai Wei, Linchuan Xu, Bokai Cao and Philip S. Yu, Cross View Link Prediction by Learning Noise-resilient Representation Consensus, WWW 2017. April 3-7, 2017. Perth, Australia. pp. 1611-1619.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, Embedding Identity and Interest for Social Networks} (Poster), WWW 2017. April 3-7, 2017. Perth, Australia. pp. 859-860.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu, On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction (Poster), WWW 2017. April 3-7, 2017. Perth, Australia. pp. 861-862.
Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S Yu, Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks, WSDM 2017. February 6-10, 2017. Cambridge, UK. pp. 741-749.
Conference PC Member
Journal Reviewer
Guest Editor
Badminton, basketball, photography, travelling, and anime