Yiyi Guo

Greetings! I am currently a PhD candidate at the Trustworthy Machine Learning and Reasoning Group, School of Computing and Information Systems, The University of Melbourne, advised by Dr. Feng Liu, Dr. Vassilli Kitsios, Dr. Liuhua Peng and Dr. Tingru Cui.

My research focuses on deep learning-based bias correction for weather forecasting, aiming to improve forecast reliability through robust and intelligent learning algorithms. The project involves addressing systematic errors in long-range prediction models by developing data-driven correction frameworks — a timely and impactful direction in the context of climate change and growing interest in trustworthy AI.

I received my undergraduate degree from McGill University, and I’m now proud to continue my journey in research and discovery here in Melbourne.

selected publications

Improved machine learning estimation of surface turbulent flux using interpretable model selection and adaptive ensemble algorithms over the Horqin Sandy Land area

Jing Zhao, Yiyi Guo, Hongsheng Zhang, and 3 more authors

Atmospheric Research, 2025

Abs DOI

The turbulent exchanges between the land surface and atmosphere, crucial for global climate change and atmospheric circulation, are typically represented through bulk formulae based on Monin-Obukhov similarity theory (MOST), using simple regression as a function of the non-dimensional stability parameter derived from limited field experiments, which leaves large uncertainties. Recently, machine learning is anticipated as an alternative or complement to bulk algorithms, leveraging its ability to detect nonlinear relationships in large datasets without constraints from the similarity relationships and self-correlations prescribed in MOST. However, there are still unresolved problems and gaps, even though common models like random forest and neural networks can be directly applied. This study proposes a hybrid approach for improved estimation of surface turbulent flux, consisting of meta-learner estimation, interpretable model selection, and adaptive model integration. Motivated by understanding how different machine learning algorithms perform as surface-layer flux estimators and further exploring how to utilize results from multiple meta-learners for better estimations, the method starts with eight different machine learning algorithms. Then, a combination of Elastic Net and Shapley Additive Explanations is developed as an interpretable model selection module, followed by an adaptive model integration using AdaBoost and extreme learning machine. Experiments at the continuous observation station in the Horqin Sandy Land area, Inner Mongolia, China, demonstrate that the proposed system delivers reliable and stable performance, significantly reducing estimation bias of three scaling parameters, with root mean square error reductions of 43.16%–56.97% compared to MOST, and outperforming the best single machine learning model with additional error reductions of 4.24%–7.90%.