Wenqin Liu

Wenqin Liu

she/her

PhD Candidate, University of Melbourne. My research focuses on Causal Discovery and Machine Learning for robust and identifiable models.

Wenqin Liu – Causal Discovery & Machine Learning

Welcome! I am Wenqin Liu, a Ph.D. candidate in Science at the University of Melbourne, where I am advised by Dr. Mingming Gong, Prof. Howard Bondell, and Dr. Qiuhong Ke. My research lies at the intersection of causal discovery, machine learning, and spatiotemporal modeling, with a particular focus on developing robust and identifiable frameworks for complex, dynamic systems.

In 2024–2025, I had the valuable opportunity to visit UC San Diego, working under the guidance of Prof. Biwei Huang. This experience has been instrumental in shaping my thinking around methodological rigor and new directions in causal modeling.

🎓 Education

  • PhD in Science, University of Melbourne (2022–Present)
    Causal Discovery & Machine Learning
    Supervisors: Dr. Mingming Gong, Prof. Howard Bondell, Dr. Qiuhong Ke

  • Master of Data Science, University of Melbourne (2020–2022)
    First Class Honours – Thesis: Multimodal Representation Learning for Volatility Forecasting Supervisor: Dr. Mingming Gong

  • Bachelor of Commerce, University of Melbourne (2016–2018)
    Majors: Actuarial Studies, Finance

🧪 Research Experience

  • PhD Researcher, University of Melbourne
    Developing causal discovery methods that handle challenges such as heteroscedasticity and missing data, and building theoretical frameworks for modeling latent spatiotemporal dynamics using partial differential equations.

  • Visiting Student Researcher, UC San Diego (2024–2025)
    Co-led skewness-based score research under Prof. Biwei Huang. Focused on both theoretical justifications and empirical evaluations.

  • Academic Tutor, University of Melbourne (2023–Present)
    Designed and taught tutorials and assessments for Statistical Modelling, Decision Making, and Machine Learning postgraduate courses.

📄 Publications

  • Liu et al. “MissScore: High-Order Score Estimation in the Presence of Missing Data.” ICML 2025
  • Liu et al. “Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models.” CLeaR 2024

For a complete list, visit the publications page.

🛠 Skills & Languages

  • Programming: Python, PyTorch, TensorFlow, R, SQL
  • Languages: English (Professional), Mandarin (Native), Cantonese (Native)

🏆 Honors & Awards

  • Melbourne Global Scholars Award 2018
  • Melbourne Research Scholarship 2022
  • Professor Maurice Belz Scholarship 2024

📬 Contact

Explore the tabs to learn more about my work in research, teaching, and academic writing. I’m always open to research collaborations and discussions. If you’re interested in working together, please don’t hesitate to get in touch.

Thank you for visiting!