Kan Ren (任侃)
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Kan Ren, Assistant Professor Office: Building 1C 303.A, |
Kan Ren is an Assistant Professor at VDI Center within the School of Information Science and Technology at ShanghaiTech University. Previously, he held a position as a Senior Researcher at Microsoft Research Asia. His research primarily focuses on machine learning and reinforcement learning, with diverse applications in data science fields such as spatiotemporal data, language, and vision. Kan is committed to addressing practical issues in data science and innovating machine learning methods, including but not limited to data mining, foundational model innovation, automated machine learning, and interpretable machine learning. He has published over 40 research papers in internationally renowned conferences and journals, including NeurIPS, ICLR, and KDD. Kan has written a book on recommender systems and he has also played a pivotal role in leading or contributing to several significant open-source projects, including Sequence Machine Learning, Automated Machine Learning, Automated Reinforcement Learning, AI for Healthcare, and AI for Finance. As a mentor, Ren Kan has nurtured and guided over ten internship students, many of whom have successfully gained admission to renowned institutions such as MIT, CMU, University of California San Diego, and University of Maryland. Additionally, many students under his guidance have received many special offers from top-tier technology companies including Huawei, Alibaba, Tencent, ByteDance, Meituan, and Xiaohongshu.
Prospective students: I'm now looking for prospective PhD/Master students and student interns to work together about (i) machine learning on sequence data (time series, texts and decision sequences); (ii) understanding and innovating foundation models like large language models and vision models, with cutting-edge machine learning and reinforcement learning technologies. Please drop me an email with your resume and transcript if you are interested.
Openings for Postdoctoral and Research Assistant positions are available.
课题组正在招收保研、统考研究生（人工智能与机器学习方向硕士、博士），请有意向的同学通过邮件发送简历与成绩单，并简要介绍成绩与科研、竞赛经历。
Book “Recommender System: Frontiers and Practices”: link (Chinese), link (English), introduction.
Please check our latest research about machine intelligence on sequence data paradigm.
2024.3 - Our paper about large-language model for machine learning has been awarded as Outstanding Paper in EACL 2024!
2024.1 - Our preliminary exploration about LLM for machine learning has been accepted in EACL 2024.
2024.1 - One paper about interpretable time series modeling has been accepted in ICLR 2024.
2023.12 - I will join ShanghaiTech University as an Assistant Professor in late 2023.
2023.9 - Two papers about irregular time-series modeling and EEG model pretraining have been accepted in NeurIPS 2023.
2023.8 - Our work about AIGC through brain-computer interface (BCI) has been online. Please checkout the paper and English or Chinese articles.
2023.7 - Checkout the latest Chinese article about our research of RL for Finance and Qlib toolkit! (WeChat, MSRA News)
2023.5 - Our work about AI for healthcare on neonatal seizure detection has been accepted in SMC 2023.
2023.5 - Our work about reinforcement learning for finance has been accepted in KDD 2023.
2023.4 - Our work MLCopilot about LLMs for automated machine learning has been online.
2023.4 - One paper about brain-inspired neural network has been accepted in ICML 2023.
2023.3 - The website of our SeqML research (machine intelligence on sequence data) has been online.
2023.1 - One paper about enseble learning for pretrained models has been accepted in ICLR 2023.
Research
Machine learning: automated machine learning and reinforcement learning.
Data mining: spatiotemporal data modeling and sequence models.
Foundation model: large language model and brain-inspired machine intelligence.
Publications
Preprint
Benchmarking Data Science Agents, Y. Zhang, Q. Jiang, X. Han, N. Chen, Y. Yang, K. Ren (corresponding).
Selected
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks, L. Zhang, Y. Zhang, K. Ren (corresponding), D. Li, Y. Yang (EACL Outstanding Paper Award)
The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 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.
Thesis
Modeling and Decision Optimization in Real-time Bidding Advertising (Chinese) (PDF)
Topical
Chronological
Academic Service
PC member: ICML, NeurIPS, AAAI, SIGIR, etc.
Journal Reviewer: JMLR, TKDE, NeuralComputing, etc.
Education
Ph.D. in Computer Science, Shanghai Jiao Tong University, June 2019.
B.Eng. in Computer Science, Zhejiang University, June 2014.
Minor in Intensive Training Program of Innovation and Entrepreneurship, Chu Kochen Honors College at Zhejiang University, June 2014.
Exchange program to University of California Davis, Feburary 2012.