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.
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
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.
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).
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).