Dr. Tiantian He

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

I'm a Senior Research Scientist and Early Career Investigator with the 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). I obtained my PhD degree from Hong Kong Polytechnic University in 2017. My 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. F. Bi, T.T. He, Y.S. Ong, and X. Luo, “Discovering spatio-temporal-individual coupled features from nonstandard tensors-A novel dynamic graph mixer approach,” IEEE TNNLS, 2025 (Accepted).
  2. F. Bi, T.T. He, and X. Luo, “Spatiotemporal graph neural network-incorporated latent factorization of tensors for dynamic QoS estimation,” IEEE/CAA JAS, 2025 (Accepted).
  3. M. Chen, X. Wu, Q. Liu, T.T. He, Y.S. Ong, Y. Jin, Q. Lao, and H. Yu, “Voronoi-grid-based Pareto Front learning and its application to collaborative federated learning,” ICML, 2025.
  4. Y. Lei, L. Shen, Z. Sun, T.T. He, S. Feng, G. Liu, “COSTA: Contrastive spatial and temporal debiasing framework for next POI recommendation,” Neural Networks, 2025.
  5. F. Bi, T.T. He, Y.S. Ong, and X. Luo, “Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data,” IEEE TKDE, 2025. (Pytorch Implementation)
  6. 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)
  7. 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. (PDF)
  8. 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.
  9. 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)
  10. H. Zhou, T.T. He, Y.S. Ong, G. Cong, and Q. Chen, “Differentiable clustering for graph attention,” IEEE TKDE, 2024. (Pytorch implementation)
  11. 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)
  12. 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 TSC, 2023.
  13. F. Bi, T.T. He, Y. Xie, and X. Luo, “Two-stream graph convolutional network-incorporated latent feature analysis,” IEEE TSC, 2023.
  14. Y. Xie, Y. Liang, M. Gong, Y.S. Ong, K. Qin, and T.T. He, “Semi-supervised graph neural networks for graph classification,” IEEE TCYB, 2023.
  15. 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.
  16. Z. Guo, Y.S. Ong, T.T. He, and H. Liu, “Co-learning Bayesian optimization,” IEEE TCYB, 2022.
  17. T.T. He, Y.S. Ong, and L. Bai, “Learning conjoint attentions for graph neural nets,” NeurIPS, 2021. (PDF) (PyTorch Implementation)
  18. T.T. He, L. Bai, and Y.S. Ong, “Vicinal vertex allocation for matrix factorization in networks,” IEEE TCYB, 2021. (PDF) (Matlab Implementation)
  19. 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 TCYB, 2020. (PDF) (Matlab Implementation)
  20. T.T. He, and K.C.C. Chan, "Measuring boundedness for protein complex identification in PPI networks," IEEE/ACM TCBB, 2019. (PDF)
  21. T.T. He, and K.C.C. Chan, “Discovering fuzzy structural patterns for graph analytics,” IEEE TFS, 2018. (PDF) (Matlab Executable)
  22. T.T. He, and K.C.C. Chan, “MISAGA: an algorithm for mining interesting subgraphs in attributed graphs,” IEEE TCYB, 2018. (PDF) (Matlab Executable)
  23. T.T. He, and K.C.C. Chan, “Evolutionary graph clustering for protein complex identification,” IEEE/ACM TCBB, 2018. (PDF) (Java Executable)
  24. 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

Student (Co-)Supervision

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