cv

Curriculum vitae of Kangning Diao.

Contact Information

Name Kangning Diao
Professional Title BCCP Fellow, UC Berkeley
Email kangningdiao@gmail.com
Phone +86-13260257366
Location Campbell Hall 341, Berkeley, California
Website https://dkn16.github.io

Professional Summary

Astrophysicist and cosmologist with expertise in 21 cm cosmology, weak lensing, Galactic synchrotron emission, differentiable simulations, and machine learning for astrophysics.

Experience

  • 2025 - 2027
    BCCP Fellow
    UC Berkeley
    Berkeley Center for Cosmological Physics postdoctoral fellow.
    • Advised by Prof. Uros Seljak
    • Research in cosmology, weak lensing, 21 cm line, Galactic synchrotron, differentiable simulations, and machine learning

Education

  • 2020 - 2025

    Beijing, China

    PhD
    Tsinghua University
    Astronomy
  • 2024 - 2025

    Berkeley, USA

    Visiting Student
    UC Berkeley
    Cosmological Physics
  • 2016 - 2020

    Beijing, China

    BS
    Tsinghua University
    Physics

Awards

  • 2024
    First Class Comprehensive Scholarship (Xiaomi)
    Tsinghua University

    First class comprehensive scholarship funded by Xiaomi, Tsinghua University.

  • 2023
    Second Class Comprehensive Scholarship (Xiaomi)
    Tsinghua University

    Second class comprehensive scholarship funded by Xiaomi, Tsinghua University.

  • 2023
    Outstanding Teaching Assistant
    Department of Physics, Tsinghua University

    Awarded for outstanding performance as a teaching assistant.

Skills

Astrophysics (Expert): 21 cm Cosmology, Weak Lensing, Galactic Synchrotron, Reionization, Component Separation
Computational Methods (Expert): Differentiable Simulations, Machine Learning, Generative Models (GAN, Flow), JAX, GPU Computing, Bayesian Inference

Languages

Chinese : Native speaker
English : Fluent

Interests

Astronomy & Cosmology: Cosmology, Weak Lensing, 21 cm Line, Galactic Synchrotron, Epoch of Reionization
Statistics & Machine Learning: Differentiable Simulations, Generative Models, Gradient-Based Sampling, Simulation-Based Inference