Jingzhe Shi / 史景喆

Hi! I'm a senior undergraduate student majoring Computer Science and Technology at IIIS, Tsinghua University (a.k.a Yao Class, directed by the Turing Award Laureate Andrew Chi-Chih Yao).

I am privileged to be mentored by Prof. Xiaolong Wang at UCSD and Prof. Hang Zhao at Tsinghua University.

In addition, I am privileged to meet with a group of talented friends when attending Physics Olympiads, and we founded CPHOS to provide Physics Olympiad simulations for high school contestants for free through an online platform. With some talented friends I met at CPHOS, we also conducted some interesting researches related to it, including the CHOPS project.

I enjoyed learning Physics and contributing to Physics Olympiads. I won a gold medal at IPhO 2021 (ranking 10th globally), and I was an invited online marker for IPhO 2022.

📬 Email  |  📑 CV  |  🎓 Google Scholar  |  💻 Github

profile photo

News

  • 2024-09: ✨ Our paper Scaling Law for Time Series Forecasting was accepted by NeurIPS 2024!
  • 2024-07: Our paper CHOPS was accepted by COLM 2024!

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    Personal Interest

    I am particularly interested in improving Physics Olympiads and doing DL/ML researches, and I take my interests very seriously.

    After attending CPhO in high school and winning a gold medal at IPhO, I have been committed to popularizing educational resources for Physics Olympiads, thus I have worked hard to found CPHOS.
    Amazed by the current progress of artifitial intelligence I learnt in college, I have been involved in many researches related to DL/ML.

    Research Interest

    My research interests lie broadly in Deep Learning and Machine Learning.

    For Applications of DL/ML, I am interested in derivatives of LLMs in all aspects, including Multi-agent researches and Multimodality LMs.

    For Physics of DL/ML, I am interested especially for Physics of Large Neural Networks, especially for Scaling Law and its explanation in various areas and scalable models. As a gold medalist in IPhO, I find it interesting to study theoretically and experimentally the impact of dataset size, model size as well as other physical quantities (e.g. context length) on model performance.

    Publications

    (* for equal contribution)
    PontTuset Scaling Law for Time Series Forecasting
    Jingzhe Shi*, Qinwei Ma*, Huan Ma, Lei Li

    NeurIPS 2024 (poster, main track)
    Code / arXiv / OpenReview

  • We proposed a theoretical framework for Scaling Law for Time Series Forecasting, taking into account look back horizon as well as dataset size and model size.
  • We conducted experiments to validate our theory proposed and assumptions made.
  • Our key theoretical and experimental findings were that optimal look back horizon does exist and it increases with dataset size, calling for a more fair comparison when proposing new time series forecasting models.

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    PontTuset CHOPS: CHat with custOmer Profile Systems for Customer Service with LLMs
    Jingzhe Shi, Jialuo Li, Qinwei Ma, Zaiwen Yang, Huan Ma, Lei Li

    COLM 2024 (poster)
    Code / arXiv / OpenReview

  • We proposed CHOPS, an LLM agent designed to efficiently access user information, interact with existing systems, and provided accurate, safe responses by leveraging a combination of small and large LLMs. Validated using the CPHOS-dataset we proposed in the same work, CHOPS demonstrated its potential to enhance or replace human customer service.

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    Preprints

    (* for equal contribution)
    PontTuset Large Trajectory Models are Scalable Motion Predictors and Planners
    Qiao Sun, Shiduo Zhang, Danjiao Ma, Jingzhe Shi, Derun Li, Simian Luo, Yu Wang, Ningyi Xu, Guangzhi Cao, Hang Zhao

    arXiv 2023
    Code / arXiv

  • We leveraged successful backbones in NLP for trajectory prediction, demonstrating scalability on diverse datasets and achieving state-of-the-art performance on Nuplan dataset.
  • I was responsible for the decoder part. I ustilized DDPM to generate trajectory in Key Point Space to capture multi-modal distribution of future trajectories.

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    Social Work Experience

    CPHOS
    2020.12 - Present
    Co-founder, Former Techgroup Leader, Council Member
  • CPHOS is an academical non-profit organization dedicated to providing Physics Olympiad simulations for high school contestants for free through an online platform.
  • CPHOS was founded in the late 2020 by a group of 10 (including myself), now it has 100+ members. 1000+ students from 200+ high schools participate in most Olympiads held by CPHOS.
  • I led the tech group to develope tools supporting online Olympiads, as well as conducting interesting researches including the CHOPS project.
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    Education Experience



    UCSD
    2024.02 - 2024.06
    Visiting researcher
    Research Advisor: Prof. Xiaolong Wang.
    Tsinghua University
    2021.09 - Present
    Undergraduate Student
    Research Advisor: Prof. Hang Zhao.

    Representative Honors and Awards

    (complete list can be found in my CV)

  • 2021: First-Class Freshmen Scholarship of Tsinghua University.

  • 2021: Gold Medalist 🏅 in the 51st International Physics Olympiad (IPhO 2021), ranking tenth globally.

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    Service

  • ICLR 2025 Reviewer

  • IPhO 2022 (the 52nd International Physics Olympiad) Marker. In that year IPhO was held in Switzerland, but due to pandemic IPhO had to invite extra markers. I was invited and fulfilled my job as an online marker to mark, discuss with my marker partner and to do rebuttal with team leaders from countries and regions all around the world through online meetings. I feel super lucky to have contributed to this top-tier Physics Olympiad as marker just one year after I attended it as contestant and won a gold medal.

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    This homepage is designed based on Jon Barron's homepage and deployed on GitHub Pages. Last updated: Oct, 2024.
    © 2024 Jingzhe Shi