Xiaoman Lu 😊
Xiaoman Lu

PhD student in Computer Science

Ubiquitous & Visual Computing (UV) Lab

I am currently a PhD student in Computer Science at the University of Warwick, and I am part of the UVLab. My research areas are artificial intelligence and computer vision, with a focus on DeepFake detection.

Download Résumé

Experience

  1. Front-end Developer

    Neusoft Corporation -Shenyang, China

    Responsibilities include:

    • Contributed to the development and design of Neusoft’s cross-border e-commerce platform
    • Responsible for the automobile Hi Map project, including programming the front-end of the electronic map, and realizing the functions of automatic location marking and generating the optimal route, etc.

  2. AI+RPA Engineer

    Intelligence Indeed Technology Company -Hangzhou,China

    Responsibilities include:

    • Developed a web crawler program to gather user reviews for three different mobile products
    • Created an informative visual data analysis dashboard
    • Integrated CRNN into character recognition

Education

  1. PhD Computer Science

    University of Warwick
    Thesis on Developing Robust and Generalized DeepFakes Detection Algorithms. Supervised by Prof Guan Yu.
    Read Thesis

  2. BSc Mathematics

    Northeastern UNiversity

    GPA: 90.44/100.00

    Courses included:

    • Mathematical Modeling
    • Numerical analysis
    • Machine Learning
Skills & Hobbies
Technical Skills
Python
RStudio
PyTorch
Hobbies
Traveling
Cats
Photography
Awards
Honorable Mention in the 2023 Mathematical Contest in Modeling (MCM)
The Consortium for Mathematics and its Applications (COMAP) ∙ February 2023
Responsible for modeling and writing the paper, and combining multiple linear regression prediction and entropy weighting-TOPSIS method to establish statistical indicators and build a model for predicting forest CO2 emissions.
Honorable Mention in the 2022 Mathematical Contest in Modeling (MCM)
The Consortium for Mathematics and its Applications (COMAP) ∙ February 2023
  • To address the issue of measuring and mitigating light pollution in diverse locations, we have developed two distinct models: the Light Pollution Level Evaluation Model and the Optimal Light Pollution Intervention Model.
  • We established a comprehensive four-level evaluation indicator system, acquired the central standard value matrix through the K-means clustering algorithm, and the Entropy-weighted TOPSIS method. These efforts culminated in the successful completion of our research article titled Chasing Better Strategies for Light Pollution Intervention.
First Prize in the 16th “Challenge Cup” College Students Extracurricular Academic and Technological Works
∙ December 2023
This project relied on a new type of digital image hiding system with strong robustness as the core product, and promoted the image hiding technology in the form of Web pages and cell phone applications, so as to encrypt the personal and corporate information, thus realizing the purpose of information security.
Languages
100%
Chinese
75%
English