Dr. Tiantian He

Center for Frontier AI Research (CFAR), Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)

Tiantian He is currently a Senior Research Scientist at Center for Frontier AI Research (CFAR), Institute of High Performance Computing (IHPC), Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), supervised by Prof. Yew-Soon Ong. Tiantian He obtained his PhD and MSc degrees from Department of Computing, The Hong Kong Polytechnic University, advised by Prof. Keith C.C. Chan. His research interests include AI, Computational Intelligence, Graph Deep Learning, Data-Centric Transfer Optimization, and Data Mining.

Email: firstname.lastname[at]outlook.com, Github

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Selected Publications

  1. H. Zhou, W. Huang, Y. Chen, T.T. He, G. Cong, and Y.S. Ong, “Road network representation learning with the Third Law of Geography,” NeurIPS, 2024. (PDF)
  2. M. Chen, X. Wu, X. Tang, T.T. He, Y.S. Ong, Q. Liu, Q. Lao, and H. Yu, “Free-rider and conflict aware collaboration formation for cross-silo federated learning,” NeurIPS, 2024.
  3. Z. Li, X. Wu, X. Tang, T.T. He, Y.S. Ong, M. Chen, Q. Liu, Q. Lao, X. Li, H. Yu, “Benchmarking data heterogeneity evaluation approaches for personalized federated learning,” FL@FM-NeurIPS, 2024.
  4. T.T. He, Y. Liu, Y.S. Ong, X. Wu, and X. Luo, “Polarized message-passing in graph neural networks,” Artificial Intelligence, 2024. (PDF) (Pytorch Implementation)
  5. H. Zhou, T.T. He, Y.S. Ong, G. Cong, and Q. Chen, “Differentiable clustering for graph attention,” IEEE Transactions on Knowledge and Data Engineering, 2024. (Pytorch implementation)
  6. S. Tan, H. Cheng, X. Wu, H. Yu, T.T. He, Y.S. Ong, C. Wang, and X. Tao, “FedCompetitors: harmonious collaboration in federated learning with competing participants,” AAAI, 2024. (PDF)
  7. F. Bi, T.T. He, and X. Luo, “A fast nonnegative autoencoder based approach to latent factor analysis on high dimensional and incomplete data,” IEEE Transactions on Services Computing, 2023.
  8. F. Bi, T.T. He, Y. Xie, and X. Luo, “Two-stream graph convolutional network-incorporated latent feature analysis,” IEEE Transactions on Services Computing, 2023.
  9. Y. Xie, Y. Liang, M. Gong, Y.S. Ong, K. Qin, and T.T. He, “Semi-supervised graph neural networks for graph classification,” IEEE Transactions on Cybernetics, 2023.
  10. F. Bi, T.T. He, and X. Luo, “A two-stream light graph convolution network-based latent factor model for accurate cloud service QoS estimation,” IEEE ICDM, 2022.
  11. Z. Guo, Y.S. Ong, T.T. He, and H. Liu, “Co-learning Bayesian optimization,” IEEE Transactions on Cybernetics, 2022.
  12. T.T. He, Y.S. Ong, and L. Bai, “Learning conjoint attentions for graph neural nets,” NeurIPS, 2021. (PDF) (PyTorch Implementation)
  13. T.T. He, L. Bai, and Y.S. Ong, “Vicinal vertex allocation for matrix factorization in networks,” IEEE Transactions on Cybernetics, 2021. (PDF) (Matlab Implementation)
  14. T.T. He, Y. Liu, T.H. Ko, K.C.C. Chan, and Y.S. Ong, "Contextual correlation preserving multi-view featured graph clustering," IEEE Transactions on Cybernetics, 2020. (PDF) (Matlab Implementation)
  15. T.T. He, and K.C.C. Chan, "Measuring boundedness for protein complex identification in PPI networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019. (PDF)
  16. T.T. He, and K.C.C. Chan, “Discovering fuzzy structural patterns for graph analytics,” IEEE Transactions on Fuzzy Systems, 2018. (PDF) (Matlab Executable)
  17. T.T. He, and K.C.C. Chan, “MISAGA: an algorithm for mining interesting subgraphs in attributed graphs,” IEEE Transactions on Cybernetics, 2018. (PDF) (Matlab Executable)
  18. T.T. He, and K.C.C. Chan, “Evolutionary graph clustering for protein complex identification,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018. (PDF) (Java Executable)
  19. P. Hu, Z. You, T.T. He, S Li, S Gu, and K.C.C. Chan, "Learning latent patterns in molecular data for explainable drug side effects prediction," IEEE BIBM, 2018.
Full Publication List Is Available Here.

Working Experience

Senior Research Scientist

Supervisor: Prof. Yew-Soon Ong

CFAR, IHPC, SIMTech, A*STAR

AI, and Data Mining.

Jul 2023 -

Research Scientist

Supervisor: Prof. Yew-Soon Ong

CFAR, IHPC, A*STAR

AI, and Data Mining.

Nov 2021 - Jun 2023

Research Fellow

Supervisor: Prof. Yew-Soon Ong

DSAIR, School of Computer Science and Engineering, Nanyang Technological University

Data-Centric Transfer Optimization.

Jan 2019 - Oct 2021

Postdoctral Research Assistant

Supervisor: Prof. Keith C.C. Chan

The Hong Kong Polytechnic Unversity

AI and Data Mining.

Nov 2017 - Sep 2018

Research Assistant

The Hong Kong Polytechnic University

Computational Intelligence and Data Mining.

Jun 2017 - Sep 2017

Research Assistant

The Hong Kong Polytechnic University

Mining Spatial Temporal Data.

Mar 2012 - Aug 2012

Education

Department of Computing, The Hong Kong Polytechnic University

PhD
Computer Science - AI, Computational Intelligence, Data Mining, and Bioinformatics
2012 - 2017

Department of Computing, The Hong Kong Polytechnic University

MSc
Computer Science - Information Systems
2010 - 2011

North China University of Technology

B. Eng.
Computer Science and Technology
2004 - 2008

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