• 07/2022–04/2026: Ph.D. in Machine Learning (Full-time), Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
   

Work Experience：

• 2026–Present: Assistant Professor, Shenzhen Loop Area Institute (SLAI), Shenzhen, China
• 12/2020–06/2022: Senior Data Mining Researcher, NetEase Games, Guangzhou, China
• 10/2019–12/2020: Senior Machine Learning Engineer, Mobvista, Guangzhou, China
• 2016–2017: Researcher, Central Research Institute of China Southern Power Grid, Guangzhou, China
• 2011–2016: IT Engineer, Guangdong Electric Power Design Institute, Guangzhou, China

Tianjun Yao is an Assistant Professor at Shenzhen Loop Area Institute (SLAI). He received his Ph.D. in Machine Learning from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in 2026.

His research lies at the intersection of AI agents, large language models, machine learning generalization, and graph machine learning. His recent work focuses particularly on self-improving agents and multi-agent systems, efficient reasoning for large language models, robust and generalizable machine learning, and graph - based representation learning and reasoning.

Before joining SLAI, he worked as a Senior Data Mining Researcher at NetEase Games and a Senior Machine Learning Engineer at Mobvista, where he conducted research on machine learning and data mining and developed learning-based solutions for large-scale real-world applications. 

His research has been published in leading machine learning and data mining venues. His work spans graph neural networks, graph out-of-distribution generalization, model distillation, knowledge-graph reasoning, language agents, and multi-agent systems.

1. Tianjun Yao, Yongqiang Chen, Yujia Zheng, Pan Li, Zhiqiang Shen, Kun Zhang. ParamMem: Augmenting Language Agents with Parametric Reflective Memory.
   International Conference on Machine Learning (ICML), 2026.
2. Tianjun Yao, Zhaoyi Li, Zhiqiang Shen.
   HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems.
   International Conference on Machine Learning (ICML), 2026.
3. Tianjun Yao, Haoxuan Li, Zhiqiang Shen, Pan Li, Tongliang Liu, Kun Zhang. REL-RAG: Relation-Aware Retrieval-Augmented Generation for Generalizable Knowledge Graph Question Answering.
   ICML GFM Workshop, 2026.
4. Haocheng Yang, Fengxiang Cheng, Tianjun Yao, Jiajun Chai, Xiaohan Wang, Guojun Yin, Wei Lin, Mengyue Yang, Yisen Wang, Fenrong Liu, Haoxuan Li, Soummya Kar.
   Enhancing Complex Symbolic Logical Reasoning of Large Language Models via Sparse Multi-Agent Debate.
   International Conference on Learning Representations (ICLR), 2026.
5. Tianjun Yao, Haoxuan Li, Yongqiang Chen, Tongliang Liu, Le Song, Eric Xing, Zhiqiang Shen.
   Pruning Spurious Subgraph for Graph Out-of-Distribution Generalization. Advances in Neural Information 
   Processing Systems (NeurIPS), 2025.
6. Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang.
   Learning Graph Invariance by Harnessing Spuriosity. 
   International Conference on Learning Representations (ICLR), 2025.
7. Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang.
   Empowering Graph Invariance Learning with Deep Spurious Infomax.
   International Conference on Machine Learning (ICML), 2024.
8. Tianjun Yao, Jiaqi Sun, Defu Cao, Kun Zhang, Guangyi Chen.
   MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification.
   The Web Conference (WWW), 2024.
9. Kai Hu, Weichen Yu, Tianjun Yao, Xiang Li, Wenhe Liu, Lijun Yu, Yining Li, Kai Chen, Zhiqiang Shen, Matt Fredrikson.
   Efficient LLM Jailbreak via Adaptive Dense-to-Sparse Constrained Optimization.
   Advances in Neural Information Processing Systems (NeurIPS), 2024.
10. Tianjun Yao, Yingxu Wang, Kun Zhang, Shangsong Liang.
    Improving the Expressiveness of K-hop Message-Passing GNNs by Injecting Contextualized Substructure Information.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023.