任侃博士，现任上海科技大学助理教授，曾就职于微软亚洲研究院并担任高级研究员。他主要的研究领域包括机器学习、强化学习，特别是人工智能基础模型、生成式模型、多模态大模型与AI 智能体 (Agent)，及其在医疗健康、推荐广告与金融等领域的应用。他在机器学习与人工智能领域顶级会议与期刊发表了四十余篇论文，包括NeurIPS、ICLR、ICML、KDD、SIGIR、AAAI等；在AI Agent方向的论文荣获CCF-A类会议VIS 2024最佳论文奖、欧洲计算语言学年会EACL 2024杰出论文奖。同时，他长期担任NeurIPS、ICLR、ICML等会议领域主席，并为JMLR、TKDE等期刊担任评审，著有《推荐系统：前沿与实践》，主持及参与多项国家自然科学基金项目，并获得多项国家发明专利。目前课题组与微软研究院、北京通用人工智能研究院、蚂蚁集团、美团等公司建立了长期的学术研究合作，学生可获得头部科技公司实习机会。其指导学生顾同学获得研究生国家奖学金，房同学获得蚂蚁公司蚂蚁星头部Offer，何同学等获得美国CMU、UCB等高校博士Offer。

更多信息请参考个人主页：https://www.saying.ren/ 

Selected Publications

• Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning, Z. Zhang, Z. Shan, K. Song, Y. Li, K. Ren (corresponding).
  The Fourteenth International Conference on Learning Representations (ICLR 2026).

• MICLIP: Learning to Interpret Representation in Vision Models, Y. Shi, Z. Yang, C. Li, J. Yu, K. Ren (corresponding).
  The Fourteenth International Conference on Learning Representations (ICLR 2026).

• Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability, Y. Shi, C. Li, Y. Wang, Y. Zhao, A. Pang, S. Yang, J. Yu, K. Ren (corresponding).
  2025 Conference on Computer Vision and Pattern Recognition (CVPR 2025).

• Discovering Influential Neuron Path in Vision Transformers, Y. Wang, Y. Liu, Y. Shi, C. Li, A. Pang, S. Yang, J. Yu, K. Ren (corresponding).
  The Thirteenth International Conference on Learning Representations (ICLR 2025).

• Learning to Select In-Context Demonstration Preferred by Large Language Model, Z. Zhang, S. Lan, L. Song, J. Bian, Y. Li, K. Ren (corresponding).
  The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025 Findings).

• VerbalTS: Generating Time Series from Texts, S. Gu, C. Li, B. Jing, K. Ren (corresponding).
  The 42nd International Conference on Machine Learning (ICML 2025).

• (Best Paper Award) VisEval: A Benchmark for Data Visualization in the Era of Large Language Models, N. Chen, Y. Zhang, J. Xu, K. Ren (corresponding), Y. Yang.
  IEEE Visualization Conference (VIS 2024).

• (Outstanding Paper Award) MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks, L. Zhang, Y. Zhang, K. Ren (corresponding), D. Li, Y. Yang.
  The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024).

• TaskBench: Benchmarking Large Language Models for Task Automation, Y. Shen, K. Song, X. Tan, W. Zhang, K. Ren, et al.
  The 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

• Benchmarking Data Science Agents, Y. Zhang, Q. Jiang, X. Han, N. Chen, Y. Yang, K. Ren (corresponding).
  The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024).

• Towards Editing Time Series, B. Jing, S. Gu, T. Chen, Z. Yang, D. Li, J. He, K. Ren (corresponding).
  The 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

• EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals, X. Liu, Y. Liu, Y. Wang, K. Ren (corresponding), W. Zheng, et al.
  The 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

• Automated Contrastive Learning Strategy Search for Time Series, B. Jing, Y. Wang, G. Sui, J. Hong, J. He, Y. Yang, D. Li, K. Ren (corresponding).
  The 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024).

• CNN Kernels Can Be the Best Shapelets, Z. Qu, Y. Wang, X. Luo, W. He, K. Ren, D. Li.
  The Twelfth International Conference on Learning Representations (ICLR 2024).

• ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling, Y. Chen, K. Ren (corresponding), Y. Wang, Y. Fang, W. Sun, D. Li.
  The 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

• Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling, K. Yi, Y. Wang, K. Ren (corresponding), D. Li.
  The 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

• Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling, Z. Li, Y. Fang, Y. Li, K. Ren (corresponding), et al.
  IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023.

• Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance, Y. Fang, Z. Tang, K. Ren (corresponding), W. Liu, et al.
  Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2023.

• CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling, Y. Wang, X. Jiang, K. Ren, et al.
  Fortieth International Conference on Machine Learning (ICML-23).

• SIMPLE: Specialized Model-Sample Matching for Domain Generalization, Z. Li, K. Ren (corresponding), et al.
  Eleventh International Conference on Learning Representations (ICLR 2023).

• Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling, Y. Fang, K. Ren (corresponding), et al.
  Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23).

• Towards Inference Efficient Deep Ensemble Learning, Z. Li, K. Ren (corresponding), et al.
  Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23).

• Bootstrapped Transformer for Offline Reinforcement Learning, K. Wang, H. Zhao, X. Luo, K. Ren (corresponding), et al.
  The 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

• Reinforcement Learning with Automated Auxiliary Loss Search, T. He, Y. Zhang, K. Ren (corresponding), et al.
  The 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

• Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble, Z. Yang, K. Ren (corresponding), et al.
  The 31st International Joint Conference on Artificial Intelligence (IJCAI-22).

• Towards Generating Real-World Time Series Data, H. Pei, K. Ren (corresponding), et al.
  The 21st IEEE International Conference on Data Mining (ICDM).

• Universal Trading for Order Execution with Oracle Policy Distillation, Y. Fang, K. Ren (corresponding), et al.
  Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).

• Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling, J. Qin, K. Ren (corresponding), Y. Fang, W. Zhang and Y. Yu.
  Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020).

• Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction, K. Ren*, J. Qin*, Y. Fang*, W. Zhang, et al. (* = equal contribution)
  Proceedings of 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019).

• Deep Recurrent Survival Analysis, K. Ren, J. Qin, L. Zheng, Z. Yang, W. Zhang, L. Qiu, Y. Yu
  Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).

• Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising, K. Ren, W. Zhang, K. Chang, Y. Rong, Y. Yu and J. Wang
  IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 4, pp. 645-659, April 1 2018.

• Real-Time Bidding by Reinforcement Learning in Display Advertising, H. Cai, K. Ren, W. Zhang, K. Malialis, J. Wang, Y. Yu, D. Guo
  Proceedings of the Tenth ACM International Conference on Web Search and Data Mining Pages 661-670.

• Product-Based Neural Networks for User Response Prediction, Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, J. Wang
  2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, 2016, pp. 1149-1154.