# AboutMe


I'm Zhuyiheng Chu, a Master's student in Computational and Applied Mathematics at the University of Chicago. I hold a Bachelor's degree in Information and Computing Science from Zhejiang University.

I have a passion for solving complex problems, as demonstrated by my participation in numerous programming and mathematical competitions.

# Competitions & Awards
- **Finalist**, 2023 Mathematical Contest In Modeling (Top 1%)  
- **Second Prize**, 2022 Contemporary Undergraduate Mathematical Contest in Modeling (Top 2%)  
- **Gold Medal**, 2021 Collegiate Computer System & Programming Contest, East China Site  
- **Gold Medal**, 2021 China Collegiate Programming Contest, Guangzhou Site  
- **Silver Medal**, 2021 ICPC Asia Shanghai Regional Contest  
- **Gold Medal**, 2020 ICPC Asia Nanjing Regional Contest  
- **Gold Medal**, 2020 China Collegiate Programming Contest, Mianyang Site  


I’ve also gained industry experience during my internship at Momenta, where I applied deep learning algorithms to predict optimal vehicle paths using multimodal data. Currently, I’m working as a grader for the University of Chicago, evaluating Python algorithm design assignments for a Master’s course.

# Internship Experience
### Weride (San Jose, California)
**Software Engineer Intern** *June. 2025 – Sept. 2025*
 - Developed and optimized reinforcement learning models for smart agents, improving the adaptability and decision-making of the simulation system.

### University of Chicago  
**MPCS Grader&TA** | *Sept. 2024 – Present*  
- Course: MPCS 55001 Algorithm, MPCS 55005 Advanced Algorithms
- Graded Python-based algorithm design assignments and contributed to the development of coursework for the University of Chicago’s Master of Computer Science program.
- Organized office hours to support students’ learning.

### Momenta (Hangzhou City, China)  
**Deep Learning Algorithms Engineer Intern** | *Jan. 2024 – May 2024*  
- Using deep learning algorithms to process multimodal information perceived by the vehicle to predict the most appropriate vehicle path.
- Employing C++ to process the output results of the multimodal model to make the results more stable.
  
# Research Experience
### Reinforcement Learning for Biological Neuronal Networks
**AI+Science UChicago Hackathon** *Apr. 2025*
- Replaced traditional PCA/static state encodings with Transformer-based representations in a reinforcement learning framework, enabling Q-learning to outperform all competitors and secure **1st place** in the competition. [DSI website](https://datascience.uchicago.edu/news/2025-aiscience-hackathon-challenges-students-to-take-on-cutting-edge-scientific-problems-with-ai/)