Kan Ren (任侃)

Kan Ren, Assistant Professor Visual and Data Intelligence (VDI) Center School of Information Science and Technology ShanghaiTech University Office: Building 1C 303.A, 393 Huaxia Middle Road, Pudong New Area, Shanghai, China renkan(-AT-)shanghaitech.edu.cn rk.ren(-AT-)outlook.com Google Scholar, GitHub

• 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, data mining 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. Two of his works about large language models (LLM) on data science have been awarded as Best Paper and Outstanding Paper in top-tier conferences. 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, 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 on the below research directions. Please drop me an email with your resume and transcript if you are interested.

  • (i) Large language model, LLM-based AI agents, and explainable AI;

  • (ii) Spatiotemporal data mining and machine learning algorithms.

• 招生公告：课题组正在招收保研、统考研究生（大语言模型与语言智能体方向、数据挖掘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。

• Openings for Postdoctoral and Research Assistant positions are available.

• Book “Recommender System: Frontiers and Practices”: link (Chinese), link (English), introduction.

• Please check our latest research about machine intelligence on sequence data paradigm.

Research

• Foundation model: large language model and LLM-based agents.

• Data mining: spatiotemporal data mining and machine learning applications.

• Learning algorithm: automated machine learning, xAI, and reinforcement learning.

Selected Publications

Large Language Model and AI Agents

• (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).

• 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.

• 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.

• 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).

• 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).

Spatiotemporal Data Mining and Machine Learning

• 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).

• 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).

• 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).

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

• 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).

Recommender System

• 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.

• 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).

• 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.

Thesis

• Modeling and Decision Optimization in Real-time Bidding Advertising (Chinese) (PDF)

Topical

• Full list is here.

Chronological

• Full list is here.

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.