Statistical learning under complex data conditions
Semi-supervised inference, informative missingness, mixture models, robust estimation, kernel methods and regularisation.
Statistical learning · Computational statistics · Forecasting
Jinran Wu
Statistical Data Scientist.
My work connects methodological statistics with real-world problems in energy, environmental science, geoscience and engineering—especially where uncertainty, missingness and complex structure matter.
Research programme
Methods for complex data, built for consequential applications
Computational statistics and optimisation
Efficient algorithms for high-dimensional and non-convex models, including stochastic, iterative, evolutionary and swarm-based methods.
Forecasting and causal dynamics
Probabilistic forecasting, uncertainty quantification and causal mechanisms in energy, environmental and socio-economic systems.
Research FellowThe University of Queensland
PhD in StatisticsQueensland University of Technology
Methods → ImpactFrom theory to real systems
Brisbane, AustraliaOpen to international collaboration
Selected work
Recent and foundational publications
2026
Informative missingness and its implications in semi-supervised learning
J. Wu, Y.-G. Wang & G. J. McLachlan · The Innovation Informatics
2026
AI ethics in geoscience: Toward trustworthy and responsible innovation
J. Wu, X. Tian, Y.-G. Wang et al. · Geography and Sustainability
2023
A new algorithm for support vector regression with automatic selection of hyperparameters
Y.-G. Wang, J. Wu, Z.-H. Hu & G. J. McLachlan · Pattern Recognition
2023
Robust regression for electricity demand forecasting against cyberattacks
D. VandenHeuvel, J. Wu & Y.-G. Wang · International Journal of Forecasting
2022
A physics-informed statistical learning framework for forecasting local suspended sediment concentrations
S. Zhang, J. Wu, Y.-G. Wang et al. · Water Research
Academic path
Training and appointments
2025–present
Research Fellow · The University of Queensland
School of Mathematics and Physics, working with Professor Geoffrey J. McLachlan.
2022–2025
Research Fellow · Australian Catholic University
Faculty of Education and Arts, working with Professor Herbert W. Marsh.
2022
PhD in Statistics · Queensland University of Technology
Thesis: Statistical Support Vector Machines with Optimizations.
Supervised by Professors You-Gan Wang, Kevin Burrage, and Yu-Chu Tian.
Collaboration
Interested in statistically rigorous work on complex real-world data?
I welcome conversations about methodological research, interdisciplinary projects, reviewing, and research supervision.