Kan Ren (任侃)

photo 

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, GitHubGitHub


  • 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。

Recent News
  • 2024.5 - Our work about LLM for Data Science has been accepted in the main conference of ACL 2024.

  • 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

Selected

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