Peiyuan Liu (劉培源)

master@THU

me.jpg

I am currently pursuing my master’s degree in Computer Science at the esteemed Tsinghua University, under the mentorship of distinguished Professors Shu-Tao Xia and Tao Dai. Before joining Tsinghua, I completed my undergraduate studies in Computer Engineering at Zhejiang University and the University of Illinois at Urbana-Champaign.

Throughout my academic journey, I’ve had the privilege of bolstering my classroom knowledge with practical industry experience at Oneflow and Xorbits, startups leading innovation in their respective fields. These experiences have provided me with a unique vantage point to tackle real-world challenges, thereby augmenting my understanding of the theoretical aspects and practical implementations of technology.

In my research, I am deeply fascinated by large language models (LLMs), long-term time-series forecasting (LTSF), and Gaussian process (GP). I am driven by the idea of utilizing these sophisticated computational methodologies to unearth deeper insights and design solutions that can have a transformative impact on society. I am committed to pushing the boundaries of these research areas and exploring the full potential of machine learning algorithms to contribute meaningfully to the world of technology.

News

Jan 15, 2024 Our paper Periodicity Decoupling Framework for Long-term Series Forecasting is accepted by ICLR 2024.
Dec 14, 2023 Our paper WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting is accepted by ICASSP 2024.
Sep 26, 2023 Our paper DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes is accepted by IJCV 2023.
Sep 1, 2023 Become a graduate student at Tsinghua University, advised by Tao Dai and Shu-Tao Xia.
Jun 30, 2023 🎉 Graduate from Zhejiang University and University of Illinois Urbana-Champaign.
May 8, 2023 Join Hangzhou Xorbits Tech Co., Ltd as an Intern in Open Source System Developer.
Feb 2, 2023 Join Beijing Oneflow Tech Co., Ltd as an Intern in Deep Learning Framework Development Engineering.

Publications

\(^*\) Equal contribution. \(^\dagger\) Corresponding author.

2024

  1. LLaTA.png
    Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation
    Peiyuan Liu\(^*\), Hang Guo\(^*\), Tao Dai\(^\dagger\), Naiqi Li\(^\dagger\), Jigang Bao, Xudong Ren, Yong Jiang, and Shu-Tao Xia
    arXiv preprint arXiv:2403.07300, 2024
    arXiv Code NPM
  2. PDF.png
    Periodicity Decoupling Framework for Long-term Series Forecasting
    Tao Dai, Beiliang Wu, Peiyuan Liu\(^\dagger\), Naiqi Li\(^\dagger\), Jigang Bao, Yong Jiang, and Shu-Tao Xia
    International Conference on Learning Representations, 2024
    Code NPM
  3. WFTNet.png
    WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
    Peiyuan Liu, Beiliang Wu, Naiqi Li\(^\dagger\), Tao Dai\(^\dagger\), Fengmao Lei, Jigang Bao, Yong Jiang, and Shu-Tao Xia
    IEEE International Conference on Acoustics, Speech and Signal Processing, 2024
    arXiv Code NPM

2023

  1. CrossMOT.png
    DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
    Shengyu Hao\(^*\), Peiyuan Liu\(^*\), Yibing Zhan, Kaixun Jin, Zuozhu Liu, Mingli Song, Jenq-Neng Hwang, and Gaoang Wang\(^\dagger\)
    International Journal of Computer Vision, 2023
    arXiv Code NPM