Currently Assistant Professor at Institute of Science Tokyo since October, 2024.

My main research focus is on bridging the gap between symbolic AI and neural networks.

During my Ph.D., I developed deep learning techniques that extract symbolic rules from time series data. These rules explain the observed data and can be used to predict and manipulate dynamic systems — a step toward explainable AI systems that provide human-accessible insights into complex behaviour.

I am now developing techniques that combine the strengths of both worlds. Symbolic AI produces knowledge that is directly readable by humans, but is brittle and hard to scale; neural networks are robust and broadly applicable, but the knowledge they encode is hard to access. By combining both approaches I aim to build intelligent systems that are simultaneously robust enough for real-world deployment and transparent enough to teach us something.

Research areas: neuro-symbolic AI, foundation models, logical rule induction, inductive logic programming (ILP), deep learning, explainable AI (XAI), time series analysis, and robustness.

Have interesting projects or want to have some discussion? You can contact me at phua [at] comp.isct.ac.jp

Goal

My main focus is on bridging the gap between symbolic AI and neural networks, combining the strengths of both to build intelligent systems that are simultaneously robust, flexible, and interpretable.

Main Publications

A Foundation Model for Zero-Shot Logical Rule Induction
Yin Jun Phua
35th International Joint Conference on Artificial Intelligence IJCAI, 2026.

Variable Assignment Invariant Neural Networks for Learning Logic Programs
Yin Jun Phua, Katsumi Inoue
18th International Conference on Neural-Symbolic Learning and Reasoning, 2024.

Class-Incremental Learning using Diffusion Model for Distillation and Replay [Best Paper Award]
Quentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi Murata
1st Workshop on Visual Continual Learning, ICCV2023, 2023.

resVAE ensemble: Unsupervised identification of gene sets in multi-modal single-cell sequencing data using deep ensembles
Foo Wei Ten, Dong Sheng Yuan, Nabil Jabareen, Yin Jun Phua, Richard Eils, Soeren Lukassen, Christian Conrad
Frontiers in Cell and Developmental Biology 11, 2023.

Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance
Yin Jun Phua, Katsumi Inoue
ILP, 203-218, 2021.

Learning Logic Programs from Noisy State Transition Data [Best Student Paper Award]
Yin Jun Phua, Katsumi Inoue
Inductive Logic Programming - 29th International Conference(ILP), JSAI2019 72-80, 2019.

Research Projects/Grants

Developing AI Systems through Data-driven Learning of Generalized Interpretable Knowledge from Background Knowledge and Limited Data
Principal Investigator ￥ 4,680,000
Grant-in-Aid for Early-Career Scientists,
Japan Society for the Promotion of Science
April 2025 - March 2028

Constraint Reasoning for Trustworthy AI
Co-Investigator ￥ 18,720,000
Grant-in-Aid for Scientific Research (B),
Japan Society for the Promotion of Science
April 2025 - March 2029

Discovering New Knowledge by Combining Symbolic Logic and Deep Learning
Principal Investigator ￥ 2,860,000
Grants-in-Aid for Scientific Research Grant-in-Aid for Research Activity Start-up,
Japan Society for the Promotion of Science
August 2022 - March 2024

Towards Reliable Generative AI by Logical Reasoning
Principal Investigator ￥ 975,000
Open Collaborative Research,
National Institute of Informatics
July 2024 - March 2025

Research on Intelligence and Consciousness
Principal Investigator ￥ 950,000
New Research Challenge Incentive Fund,
Organization for Fundamental Research, Tokyo Institute of Technology
September 2023 - March 2025

Robust AI by Integration of Knowledge Representation and Machine Learning
Co-Investigator ￥ 41,470,000
Grant-in-Aid for Scientific Research (A),
Japan Society for the Promotion of Science
April 2022 - March 2025

Understandable Model by Combination of Symbolic Machine Learning and Deep Statistical Learning
Research Fellow ￥ 1,500,000
Grants-in-Aid for Scientific Research Grant-in-Aid for JSPS Fellows,
Japan Society for the Promotion of Science
April 2021 - March 2022