Education

  1. B.S. in Computer Science and Technology

    Beijing Institute of Technology
    Focusing on advanced algorithms, machine learning, and artificial intelligence. This dual-degree program provides a strong foundation for my current research in Embodied AI and RL.

  2. B.S. in Mathematics and Applied Mathematics

    Beijing Institute of Technology
    This program laid the groundwork for my analytical and problem-solving skills, which were instrumental in my early research endeavors and success in international math competitions.
Skills & Hobbies
Technical Skills
Python

PyTorch, TensorFlow, scikit-learn

C++

Object-oriented programming, data structures, 100+ LeetCode problems

LaTeX

Academic Writing

Robotics

ROS, Gazebo, Robot Simulation

Research & Analysis
Running

Marathon enthusiast with an annual running distance of over 1000km.

Guitar

Serves as the guitarist in a rock band on campus.

Cats

Everybody loves cats, right?

Awards
Finalist (MCM/ICM)
Top 1.96% of 27,456 teams worldwide
Consortium for Mathematics and its Applications (COMAP) ∙ May 2025
This study quantifies the ecological impact of converting forests to farmland by focusing on nitrogen cycling, a key indicator of ecosystem health. We developed three dynamic models using modified Lotka-Volterra equations: a Forest Ecosystem Nitrogen Cycle Model (FENCM), an Agricultural Ecosystem Nitrogen Cycle Model (AENCM) incorporating human interventions, and a more complex Agricultural Ecosystem-Food Web-Nitrogen Cycle Model (AE-FW-NCM). Our comparative analysis of these models highlights the trade-offs between agricultural yield, biodiversity, and sustainability, and concludes with practical recommendations for farmers. The TeX source and numerical simulation code are open-sourced on GitHub.
Meritorious Winner (MCM/ICM)
Top 9.29% of 20,858 teams worldwide
Consortium for Mathematics and its Applications (COMAP) ∙ May 2023
To address the increasing frequency of droughts, we developed a series of mathematical models to predict the viability of plant communities under various climate scenarios. Our approach included a Soil-Water Model (SWM) based on Darcy’s Law and an Improved Population Lotka-Volterra Model (PLVM) that accounts for species competition. We combined these into a comprehensive Plant Soil Moisture Competition Model (PSMCM) to solve real-world problems related to drought mitigation and sustainable land-use planning. Our study provides valuable insights into the composition of drought-resistant plant communities and can assist in conservation efforts in arid regions.
Languages
100%
Chinese
75%
English
10%
German