cv

General Information

Full Name Kaijian Wang
Email kaijianwang2003 [at] gmail.com, wangkaijian [at] ustc.edu.cn

Education

  • 2021 - 2025
    B.S. in Information Security
    University of Science and Technology of China, Anhui, China

Experience

  • 2024 - Now
    Research Intern
    UC San Diego, USA
    • LLM Server System
  • 2023 - 2024
    Research Intern
    WMGroup, USTC, China
    • Adivisors Prof. Weiming Zhang, Prof. Kejiang Chen
    • Robust Image Watermark
    • Proposed a watermark recovery module based on image hiding, designed to restore information-embedded images subjected to cropping and spatial manipulation.
    • Completed experiments on the reproduction of related research and developed the code for the module framework as proposed by coworker.
  • 2023 - 2024
    Research Intern
    • Adivisors Prof. Weiming Zhang, Postdoc. Zehua Ma
    • Review on Diffusion Model Watermarking
    • Learned the principles of diffusion model in depth, and explored works involved in embedding watermarks on images generated by diffusion models.
    • Tried to design a guiding map to use the probability distribution information in the diffusion process of the Diffusion Model to optimize subsequent watermark embedding.
  • 2022 - Now
    CTF Player
    USTC-NEBULA, USTC, China
    • Member of USTC-NEBULA
    • Our team ranks 16th/1655(~0.97%) in China in the CTFTime rating during 2023-2024!
    • Mainly focus on Misc (Steganograpy and Forensics)

Projects

  • 2024
    Tiny-C Compiler
    • Developed a simplified C compiler in C, implementing lexical analysis, syntax analysis, and Intermediate Representation (IR) generation for the Tiny-C language.
    • Supported key C language features in Tiny-C, including assignment, arithmetic operations, while loops, and if-else statements.
    • Github repo Tiny-C Compiler
  • 2024
    Semantic Guidance for Diffusion Model
    • Conducted a detailed investigation into the semantic guidance of diffusion models, focusing on understanding both the theoretical underpinnings and experimental outcomes.
    • Identified existing issues within the semantic guidance provided by diffusion models, particularly emphasizing the loss of positional information.
    • Developed a straightforward method to address the loss of positional information in the semantic guidance of diffusion models, enhancing model accuracy and interpretability.
    • Blog post Semantic Guidance for Diffusion Model

Selected CTF Awards

  • 2024
    • 3rd prize(~20%) in D^3CTF
    • Top 9th(~1%) in L3HCTF
  • 2023
    • Top 3rd(~1%) in TPCTF
    • 3rd prize(~20%) in D^3CTF
    • 3rd prize(~10%) in Hackergame2023

Honors and Awards

  • 2024
    • DAS Scholarship(Top 10 in the department)
  • 2023
    • Outstanding Student Scholarship(Bronze)
  • 2022
    • Zhang Zongzhi Scholarship

Teaching Assistant

  • 2024
    Information Security Design and Practice
    USTC, China
    • Information hiding in multimedia and file analysis
  • 2023
    Data Structure and Algorithm
    USTC, China

Other Interests

  • Anime: Mushoku Tensei, Oshinoko, etc.