XU, Linchuan (许林川)

XU, Linchuan (许林川)

CTO, TheSEA

Chief Technology Officer

TheSEA Inc

E-mail: linchuan.xu (at) thesea.ai

Part-Time Project Researcher

Department of Mathematical Informatics
Graduate School of Information Science and Technology
The University of Tokyo

Biography

I am currently the CTO of TheSEA Inc and a part-time project researcher with The University of Tokyo. Prior to that, I was Research Assistant Professor with Department of Computing, the Hong Kong Polytechnic University from July 2020 to July 2023. 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.

Research Interests

Data mining, deep learning, and health informatics.

News

  • A paper about graph data augmentation is accepted to ICDM2023.

Journal Publications

2022

  • Chuan-hao Lin, Linchuan Xu, Kenji Yamanishi, Network Change Detection Based on Random Walk in Latent Space, IEEE Transactions on Knowledge and Data Engineering, to appear.

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

  • 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, to appear in Ophthalmology Glaucoma.

  • 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.

Conference Publications

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

  • Jun Huang, Linchuan Xu, Jing Wang, Lei Feng and Kenji Yamanishi, Discovering Latent Class Labels for Multi-Label Learning, to appear in IJCAI-PRICAI 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.

Teaching

  • COMP2022 Programming For Fintech Applications, 2021 Spring

Selected Services

Journal Reviewer

  • Scientific Reports
  • Neurocomputing
  • IEEE Access
  • Computational Social Networks

Selected Awards

  • 2018, PAKDD Student Travel Award
  • 2017, DSAA Best Research Paper Award
  • 2017, DSAA Student Travel Award
  • 2017, WSDM Student Travel Award