ANDONG WANG
Research Scientist · RIKEN AIP

Andong Wang 王安东

Andong is a researcher at RIKEN AIP studying tensor representation learning, often by extending foundational principles from matrices and function spaces to higher-order tensors. He hopes to develop ideas that are both conceptually deep and practically impactful.

Tensor Learning Trustworthy Learning AI for Science & Quantum ML Algebraic Geometry Methods Autonomous Driving & Robotics

🤖 In his early engineering days: things worked, but he did not know why.
🤯 In his later theory days: he knew why, but nothing worked.
🫠 His current ambition: is to combine the two traditions—neither knowing why, nor making it work.

Research Directions

A compact view of research themes around representation, robustness, transfer, and reliable learning from imperfect information.

📐 Data spaces · Tensor geometry

Algebraic and geometric structures induced by the (generalized) t-product and transform-domain operations, establishing a unified geometric foundation for tensor learning via t-scalars, t-modules, and t-manifolds.

🛡️ Parameter spaces · Low-rank robustness

Transformed low-rank parameterization for robust generalization, adversarial robustness, sample efficiency, and transferability in tensor neural networks and structured regression models.

♾️ Function spaces · Functional tensor SVD

Extending tensor decompositions from discrete arrays to infinite-dimensional function spaces, supporting functional data analysis, operator learning, and multi-output regression under combinatorial and distribution shifts.

⚛️ Applications · Quantum ML & Multimodal ML

Quantum machine learning and multimodal representation learning, leveraging tensor networks as a principled mathematical language for structured representations, prompt tuning, and robust recovery from noisy/incomplete data.

Selected Publications

Selected papers on tensor geometry, robust generalization, transfer learning, and tensor recovery.

  1. Refining dual spectral sparsity in transformed tensor singular values.
     Wang, A., Qiu, Y., Huang, H., Jin, Z., Zhou, G., & Zhao, Q. · ICML 2026. PDF Code
  2. Towards a geometric understanding of tensor learning via the t-product.
     Wang, A., Qiu, Y., Huang, H., Jin, Z., Zhou, G., & Zhao, Q. · NeurIPS 2025. PDF Code
  3. Low-Rank Tensor Transitions (LoRT) for transferable tensor regression.
     Wang, A., Qiu, Y., Jin, Z., Zhou, G., & Zhao, Q. · ICML 2025. PDF Code
  4. Generalized tensor decomposition for understanding multi-output regression under combinatorial shifts.
     Wang, A., Qiu, Y., Bai, M., Jin, Z., Zhou, G., & Zhao, Q. · NeurIPS 2024. PDF Code
  5. Transformed low-rank parameterization can help robust generalization for tensor neural networks.
     Wang, A., Li, C., Bai, M., Jin, Z., Zhou, G., & Zhao, Q. · NeurIPS 2023. PDF Code
  6. Tensor recovery via L-spectral k-support norm.
     Wang, A., Zhou, G., Jin, Z., & Zhao, Q. · IEEE Journal of Selected Topics in Signal Processing, 15(3), 522–534, 2021. Link
  7. Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms.
     Wang, A., Li, C., Jin, Z., & Zhao, Q. · AAAI 2020 Oral. PDF Code
  8. Latent Schatten TT norm for tensor completion.
     Wang, A., Song, X., Wu, X., Lai, Z., & Jin, Z. · ICASSP 2019 Oral. Link
  9. Noisy low-tubal-rank tensor completion.
     Wang, A., Lai, Z., & Jin, Z. · Neurocomputing, 330, 267–279, 2019. Link
  10. Near-optimal noisy low-tubal-rank tensor completion via singular tube thresholding.
     Wang, A. & Jin, Z. · ICDM Workshop 2017. Link (My very first paper, published after extending my PhD. It looks simple now, but it was a hard time of real growth 😂)

Education and Research Experience

Education and research appointments.

Academic Service

Selected organization, service, and reviewing activities.

  1. Secretary, RIKEN Researcher Assembly, FY2026–FY2027.
  2. Area Chair: ICML 2026, NeurIPS 2026, ICLR 2027.
  3. Senior Program Committee: AAAI 2027.
  4. Co-organizer, ICIAM 2023 Mini-symposium: New Trends in Tensor Networks and Tensor Optimization.
  5. Workshop Co-Chair, IEEE CAI 2024 Workshop: Tensor Models for Machine Learning.
  6. Reviewer for NeurIPS, ICML, ICLR, AISTATS, AAAI, IJCAI, CVPR, TPAMI, TIP, TSP, TNNLS, TCYB, TCSVT, SIIMS, Pattern Recognition, Neural Networks, and Machine Learning.

Research Grants, Projects and Awards

Competitive research funding, research initiatives, and selected recognition.