cv

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Basics

Name Jing Yang
Label Post-doctoral Researcher
Email jing.yang@tu-berlin.de
Url https://jingyng.github.io/
Summary I am a post-doctoral researcher focusing on AI applications, specifically for social good, including fact-checking, misinformation detection, and social media analysis.

Work

  • 2024.11 - 2027.10
    Postdoc Researcher
    XplaiNLP group, Quality and Usability lab, TU Berlin
    working as a post-doc researcher at TU Berlin, focusing on topics related to generating natural language languages for AI-based disinformation detection.
    • Natural Language Processing
    • Explainable AI
    • Fact-checking

Education

  • 2019.08 - 2024.11

    Campinas, Brazil

    PhD
    University of Campinas, Campinas, Brazil
    Computer Science
    • Machine Learning
    • Natural Language Processing
    • Parallel Computing
    • Computer Vision
    • Reinfocement Learning
  • 2016.09 - 2019.06

    Changsha, China

    Master
    Hunan University, Changsha, China
    Computer Science
    • Digital Forensics
    • Information Security
    • Image Processing
  • 2012.09 - 2016.06

    Wuhan, China

    Bachelor
    Hubei University of Technology, Wuhan, China
    Information and Computing Science
    • Mathematics
    • Computer Science
    • Statistics

Awards

  • 2020
    Best Master Thesis Award
    Hunan University
    Awarded for the best master thesis titled '3D printed Objects Authentification and Source Attribution based on Printing Distortion'.

Languages

Chinese (Mandarin)
Native speaker
English
Fluent
Portuguese
Basic

Projects

  • 2024.11 - 2027.10
    FakeXplain
    FakeXplain is a BIFOLD Agility Project pursues three goals: (1) Development of different explanations for the AI-based disinformation detection process for improved intelligent decision support for citizens and journalists. (2) Development of different evaluation criteria for the explanations in order to empirically investigate their evaluation in crowd-based user studies and qualitative interviews with journalists. (3) Development of an evaluation framework for AI-generated explanations that takes into account both objective and subjective evaluation components.
    • Explainable fact-checking
    • Human-AI Interection