CV
Education
MSc Applied Computational Science and Engineering, Imperial College London (2024-Present)
Department of Earth Sciences and Engineering | Current Distinction
Key Courses: Computational Mathematics (A*), Machine Learning (A*), Fluid Dynamics (A*), Deep Learning (A)BSc Mathematics and Applied Mathematics, Taiyuan University of Technology (2020-2024)
School of Mathematics | GPA: 92/100
Key Courses: Mathematical Analysis (97), Advanced Algebra (98), Numerical Analysis (94)
Awards: Academic Outstanding Individual (2020-2024)
Research Experience
Comparative Benchmarking of Reduced-Order Models for Data Assimilation Applications
MSc Independent Research Project (Jun 2025 - Present)
Leading a comprehensive benchmarking study of CAE-based reduced-order models (CAE+DMD, CAE+LinearMLP, CAE+WeakLinearMLP) for data assimilation applications across diverse physical systems.
Evaluating model performance and data assimilation effectiveness on computational fluid dynamics datasets (cylinder flow), turbulent flow systems (Kolmogorov flow), and real-world multi-physics atmospheric datasets.
Developing standardized evaluation frameworks to assess both reconstruction accuracy and assimilation quality of operator learning approaches in reduced-order modeling contexts.
Deep Learning-Based Simulation of Dispersion Shock Waves in Nonlinear PDE Systems
Research Project Leader (Dec 2023 - Dec 2024)
Outputs: Physical Review E
Led the full-cycle development of a research project, encompassing data preprocessing, model design, numerical experiments, and thesis writing.
Developed the DPINN and DRKT modules based on the PINN framework and traditional Runge-Kutta methods, innovatively addressing dispersion shock wave phenomena in the Generalized Gardner equation.
Integrated the modules into the PgMSNN model using the multi-stage training strategy.
The 32nd International Joint Conference on Artificial Intelligence (IJCAI)
Research Team Leader (Aug 2023)
Outputs: IJCAI conference
Attend the IJCAI conference and oral presentation at MiGA workshop.
Presented the technical report of the MSTCN-VAE model, an unsupervised model based on skeletal data to effectively solve the micro-gesture classification problem.
Solving Nonlinear Partial Differential Equations Based on PINN Method
Research Project Leader (Jul 2022 - Apr 2023)
Outputs: Optik
Using the PINN method to simulate the wave solutions (solitons, breathers) of Modified Schrodinger equation.
Reproduce the PINN method in the Tensorflow and Pytorch.
Build a Physical-Informed Neural Network to process the complex number output.
Competitions & Awards
FEMA Predicting the Unpredictable Challenge | Feb 2025
Team Captain | Rank 1
- Developed machine learning models for real-time lightning storm evolution prediction and location forecasting
MiGA-IJCAI Challenge (Track1 Microgesture Classification) | Apr-Jun 2023
Team Captain | Rank 12
- Led team development and optimization of unsupervised model for micro-gesture classification
China Undergraduate Mathematical Contest in Modeling | Sep 2023
Team Captain | National Second Prize
- Developed mathematical model for solar field optimization and energy efficiency calculation
ASC Student Supercomputer Challenge | Nov 2021 - Mar 2022
Team Member | Global Second Class
- Deployed Yuan1.0 model in HPC cluster for training and hyperparameter tuning
Technical Skills
Programming Languages & Frameworks
- Python (TensorFlow, PyTorch)
- MATLAB, C++ (parallel computing)
- Git, LaTeX
Scientific Computing & Machine Learning
- Physics-informed neural networks (PINNs)
- Koopman operator methods
- Data assimilation techniques
- Reduced-order modeling
Research & Professional Skills
- Mathematical modeling and analysis
- Experimental design and optimization
- Team leadership and project management
- Conference presentations
Publications
Talks & Presentations
IJCAI 2023 Oral Presentation at MiGA Workshop
Conference presentation at International Joint Conference on Artificial Intelligence (IJCAI), Macau, China