Kan Ren (任侃)


Kan Ren, Assistant Professor
School of Information Science and Technology
ShanghaiTech University

Office: Building 1C 303.A,
393 Huaxia Middle Road, Pudong New Area,
Shanghai, China

Google Scholar, GitHubGitHub

  • Kan Ren is an Assistant Professor at the School of Information Science and Technology, 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 within the realm of data science, particularly concerning time series and the general sequence data paradigm. His work finds diverse applications in fields such as brain-computer interfaces, healthcare, finance, and recommender systems. Kan is committed to solving practical problems in data science and innovation. He has authored over 40 research papers in prestigious international conferences and journals and has written a book on recommender systems. He has played a pivotal role in leading or contributing to several significant open-source projects, including Sequence Machine Learning, Automated Reinforcement Learning, AI for Healthcare Sequence Data, and AI for Finance. Kan's contributions to research have not only led to academic accolades but have also facilitated the real-world application of advanced technologies, achieving remarkable results and efficiencies. In his role as a mentor, Kan has nurtured and guided more than ten students, with many gaining admission to esteemed universities such as MIT, Carnegie Mellon University, UC San Diego, and the University of Maryland for further studies. Moreover, he has successfully prepared students who have obtained leading positions at top-tier tech firms like Huawei, Alibaba, Tencent, ByteDance, Meituan, and Xiaohongshu, reflecting the high caliber of his mentorship.

  • Prospective students: I'm now looking for prospective PhD/Master students and student interns to work together on (i) multimodal data modeling and foundation models with applications in the next generation human-computer interfaces; (ii) machine learning on time series and sequence data; (iii) autonomous decision agents based on large language models, with cutting-edge machine learning and reinforcement learning technologies. Please drop me an email with your resume and transcript if you are interested.

Recent News
  • 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.

  • 2023.9 - 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.


  • Data mining: sequence data mining and spatial-temporal data modeling.

  • Machine learning: meta-learning, AutoML and ensemble learning.

  • Reinforcement learning and its real-world applications.





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



Academic Service

  • PC member: ICML, NeurIPS, AAAI, SIGIR, etc.

  • Journal Reviewer: JMLR, TKDE, NeuralComputing, etc.