Jianxin Ma   Location: Beijing, China

Biography Updated Dec 1, 2024

• He left the Qwen team in late 2024.
• At Alibaba Group (阿里巴巴), Jianxin Ma (马坚鑫) conducted research and engineering on foundation models as a member of the Qwen team (通义千问-基础大模型), formerly known as the M6 & OFA team.
• He earned his master's degree in Computer Science and Technology from Tsinghua University (清华大学) in June 2020 and his bachelor's degree in the same field from the same university in June 2017. During his graduate studies, he conducted research under the supervision of Wenwu Zhu and Peng Cui, focusing on the development of generative, interpretable, and controllable AI techniques for structured data.

Research Interests

Building AI systems capable of interacting with the complex real world (e.g., through structured and multimodal signals) and learning from the experiences they collect (e.g., through memory, active learning, experiential learning, and automated training-data collection).

Selected Publications (see Google Scholar for a complete list)

• Qwen2 Technical Report. 2024. [model]
• Qwen-Agent. 2024. [code]
• Qwen Technical Report. 2023. [model]
• Pretrained Diffusion Models for Unified Human Motion Synthesis. 2022.
• OFA: Unifying Architectures, Tasks, and Modalities through a Simple Sequence-to-Sequence Learning Framework. ICML 2022. [code]
• M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems. 2022.
• M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers. NeurIPS 2021.
• M6: A Chinese Multimodal Pretrainer. 2021.
• Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. KDD 2021. [code]
• Disentangled Self-Supervision in Sequential Recommenders. KDD 2020.
• Learning Disentangled Representations for Recommendation. NeurIPS 2019. [poster] [code & dataset]
• Disentangled Graph Convolutional Networks. ICML 2019. [code]
• Hierarchical Taxonomy Aware Network Embedding. KDD 2018. [code]
• DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI 2018. [code][supp]