Weida Wang

Weida Wang

Fudan University

👋 Welcome!

My name is Weida Wang (zh: 王蔚达), and I also go by David. I am currently a first-year PhD student at the College of Computer Science and Artificial Intelligence, Fudan University, where I am also collaborating with the Shanghai AI Lab. My research interests primarily lie in LLM reasoning, agent for science, with a recent focus on physical science.

Prior to this, I completed my undergraduate studies in Software Engineering at the School of Computer Science and Technology, Tongji University. Over the past few years, I have gained valuable research experience in various domains, and worked closely with AP. Jin Zeng (Tongji Univ), Prof. Lin Zhang (Tongji Univ) and Prof. Guanjie Zheng (Shanghai Jiaotong Univ).

If you’d like to connect or discuss potential collaborations, don’t hesitate to drop me an email.

🔥 News

📝 Publications

  • CCF-A CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
    Weida Wang*, Dongchen Huang*, Jiatong Li*, Tengchao Yang*, Ziyang Zheng*, Chuyi Peng, …, Yunqi Cai, Xi Dai, Shufei Zhang, Lei Bai, Jinguang Cheng, Zhong Fang, Hongming Weng
    International Conference on Learning Representations (ICLR 2026).
    Paper Code

  • CCF-A Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention
    Weida Wang*, Changyong He*, Jin Zeng, Di Qiu
    International Conference on Computer Vision (ICCV 2025)
    Paper Code

  • CCF-A Step-GRPO: Internalizing Dynamic Early Exit for Efficient Reasoning
    Benteng Chen*, Weida Wang* (project leader), Shufei Zhang, Mingbao Lin, Min Zhang
    Annual Meeting of the Association for Computational Linguistics (ACL 2026)
    Paper

  • CCF-A PolyReal: A Benchmark for Real-World Polymer Science Workflows
    Wanhao Liu*, Weida Wang*, Jiaqing Xie, Suorong Yang, Jue Wang, Benteng Chen, Guangtao Mei, Zonglin Yang, Shufei Zhang, Yuchun Mo, Lang Cheng, Jin Zeng, Houqiang Li, Wanli Ouyang, Yuqiang Li
    Computer Vision and Pattern Recognition (CVPR 2026) Findings
    Paper Code

  • CCF-A TRACK: Temporal Decoupled Kriging for Inductive Spatio-temporal Graph
    Jianping Zhou, Weida Wang, Bin Lu, Guanjie Zheng, Lei Bai, Xinbing Wang, Chenghu Zhou
    Transactions on Knowledge and Data Engineering (TKDE 2026)
    Paper

    🏅 Honors and Awards

  • 2023 National Scholarship (top 0.2% nation-wide)
  • 2025 Outstanding Graduate Award of Shanghai
  • 2022,2023,2024 Merit Student in Tongji University
  • 2023 🥇 Gold Medal of International Genetically Engineered Machine Competition (iGEM) AI & Software Track
  • 2023 🥇 First Prize of National Undergraduate Mathematics Competition (Non-mathematics Category)
  • 2023 🥇 First Prize of China Undergraduate Computer Design Competition (top 0.1% nation-wide)
  • 2023 🥈 Second Prize of China Collegiate Computing Contest HCI Innovation Competition (top 0.4% nation-wide)
  • 2024 🥉Third Prize of China Collegiate Computing Contest Mobile Application Innovation Contest (top 1% nation-wide)
  • 2023 🥇 First Prize of HuaShu Cup National Undergraduate Mathematical Modeling Contest (top 2% nation-wide)
  • 2023 🥈 Honorable Mention of Mathematical Contest in Modeling
  • 2022 🥇 First Prize of Undergraduate Mathematics Competition (Non-mathematics Category) in Shanghai

📖 Educations

  • 2025-2030, College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China
  • 2021-2025, School of Computer Science and Technology, Tongji University, Shanghai, China

💻 Internships

  • 2024.10 - Present, AI for Science Group, Shanghai AI Laboratory [link]
  • 2023.07 - 2025.03, Graph Signal Processing Lab, Tongji University
  • 2023.11 - 2024.08, Intelligent Internet of Things Research Center (IIOT), Shanghai Jiao Tong University [link]
  • 2023.11 - 2024.03, City Science Lab@Shanghai (MIT Media Lab) [link]
  • 2023.03 - 2024.03, Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University [link]
  • 2023.07 - 2023.08, Hundsun Technologies Inc. [link]

🧩 Projects

CCCC HCI 2023
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ImagiTale - An Interactive Storybook Learning App for Children

Weida Wang, Leya Yang, Xin Li, Yao Zhang, Yutong Fu, Xinyi Liao

Mobile App CCCC 2023&2024 Award-Winning Work
  • ImagiTale is an AI-driven application designed for children aged 6-8 and their parents. It enhances children’s language expression and cognitive development by providing personalized picture book recommendations and encouraging active storytelling.
  • The app is developed specifically for iPad using Xcode and Swift, with a user interface designed in SwiftUI. It employs SAM for dynamic image segmentation, enhancing visual interactivity. ChatGPT generates engaging dialogues, while Swift Speech handles speech-to-text and text-to-speech conversion. Additionally, a custom emotion analysis model offers tailored feedback to improve children’s language learning through interactive storytelling.
  • This project was submitted to the 2024 China Collegiate Computing Contest - Mobile Application Innovation Contest (CCCC App), where it won the National Third Prize (top 1%) and won the National Second Prize (top 0.4%) and the Best Innovation Award in the 2023 China Collegiate Computing Contest HCL Innovation Competition (CCCC HCI).
iGEM 2023
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CASleuth - the Virus Detective

Tongji-Software Team: Weida Wang*, Shiyi Zhou*, Ziyang Zhang*, Yao Zhang, Yuxuan Wang, Yutong Chen, Yuanyi Lu, Xialu Chen, Xuanyi Liu

Wiki Software Game Video iGEM 2023 Software&AI Track Gold Medal Work
  • The COVID-19 pandemic underscored the need for rapid and accurate virus detection methods, as traditional PCR-based approaches are often time-consuming and require complex laboratory environments. In response, our project focuses on a novel detection method using the CRISPR-Cas system, which offers a more rapid, portable, and cost-effective alternative, particularly suitable for underdeveloped areas. By targeting viral RNA sequences with guide RNAs (gRNAs) designed for specific Cas proteins, this method provides a promising future for scalable and accessible virus detection.
  • To bridge the gap between cutting-edge biotechnology and public understanding, we designed an educational game called CASleuth. This game allows everyday users to learn about CRISPR-Cas technology interactively and engagingly. Alongside the game, we developed a software tool also named CASleuth, tailored for bioinformatics researchers. This tool facilitates efficient database queries and predictions of gRNA efficiencies for different viruses using a deep learning framework based on convolutional neural networks. The web platform serves to promote the project, raising awareness and encouraging adoption among both the scientific community and the general public.
  • The CASleuth project was showcased at the 2024 iGEM competition, where it earned a Gold Medal for its innovative approach in combining education with bioinformatics, enhancing public engagement and advancing virus detection research.