Dr. Kan Ren is currently an Assistant Professor at ShanghaiTech University and previously served as a Senior Researcher at Microsoft Research Asia. His research focuses on machine learning and reinforcement learning, with particular emphasis on foundation models for artificial intelligence, generative models, multimodal large models, and AI agents, as well as their applications in healthcare, recommender systems and advertising, and finance. 

He has published more than forty papers at top-tier conferences and journals in machine learning and artificial intelligence, including NeurIPS, ICLR, ICML, KDD, SIGIR, and AAAI. His work on AI agents has received the Best Paper Award at IEEE VIS 2024, a CCF-A conference, and the Outstanding Paper Award at EACL 2024. He has also served extensively as an Area Chair for major conferences such as NeurIPS, ICLR, and ICML, and as a reviewer for leading journals including JMLR and TKDE. He is the author of "Recommender Systems: Frontiers and Practice", has led and participated in multiple projects funded by the National Natural Science Foundation of China, and holds multiple Chinese invention patents. 

His research group currently maintains long-term academic collaborations with Microsoft Research, the Beijing Institute for General Artificial Intelligence, Ant Group, Meituan, and other leading organizations. Students in his group have consistently secured internship opportunities at top technology companies. Among his advisees, Mr. Gu received the National Scholarship for Graduate Students, Mr. Fang received a top-tier offer through Ant Group’s Ant Star program, and Mr. He, among others, received PhD offers from universities such as Carnegie Mellon University and the University of California, Berkeley.

Further information can be found on Kan's personal homepage: 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.