About me
Hello there! I'm Xun, a graduate of the College of Civil Engineering at Tongji University (June 2025), and currently a Research Assistant in the same department.
My academic journey began with a Bachelor's degree in Hydraulic Engineering from Northwest A&F University, where I studied everything related to hydraulic engineering complex and its interaction with the environment. During my Master studies, I discovered a more creative fusion between Data Science and Hydrogeology. My Master’s research focused on groundwater flow and solute transport modeling, with my thesis centered on developing new deep learning-based parameterization methods (DLPMs) and building deep learning surrogate models for predicting groundwater solute transport.
Feel free to reach out for discussion, collaboration, or any exciting opportunities!
I plan to pursue a PhD degree in the near future.
E-mail: 2232324[at]tongji[dot]edu[dot]cn
💻 Current Research Interest
Groundwater Modelling, Urban Flooding, Scientific Machine Learning, Inverse Problem, Data Assimilation
As I am still at an early stage of my research career, I explored a broad range of topics. Some representative examples include:
Methods side: data assimilation; inverse problems (parameter estimation; interpolation and reconstruction); generative modeling (DDPM, VAE, GAN, flow matching); surrogate modeling (reduced-order modeling); physics-informed neural networks; operator learning; transfer learning; reinforcement learning; graph learning; uncertainty quantification; federated machine learning; causal inference; geostatistics; time-series forecasting models (statistical models such as ARIMA, ensemble machine learning methods such as XGBoost/LightGBM, and deep neural networks such as GRU/LSTM/Transformer); interpretable machine learning (SHAP, Grad-CAM); upscaling methods for geologic models; coupled surface water–groundwater modeling...
Applications side: groundwater contamination source identification and high-resolution characterization of hydraulic conductivity fields; groundwater well placement optimization;groundwater level prediction; urban flooding; computational fluid dynamics; atmospheric pollution modeling (Gaussian plume models); seismic waveform inversion; structural health monitoring; inverse design of materials; battery state estimation and structural design of fuel cell catalyst layers; debris floods; inversion of groundwater storage from satellite gravimetry; image-based sediment detection; remote sensing (carbon sources and sinks in lakes); Arctic sea ice...