Instructor Team

Selected instructors are listed below.

Instructor J

Academic Background

Instructor J is a former technical expert at Alibaba and has worked on the risk control system architecture at Ping An Bank.

His main areas of expertise include algorithms and system optimization. He holds both a bachelor's and a master's degree in Computer Science from East China Normal University and was recognized as an Outstanding Graduate of Shanghai.

With 16 years of full-stack development experience, his technical background covers embedded systems, high-performance C++ computing, and distributed systems. He also holds seven national technical patents.

Relevant Experience

In the 2024 season, eight of his students advanced to the Gold Division, including two middle school students. The shortest training period was only eight weeks.

He developed an original "algorithmic thinking breakdown method," helping students progress from Bronze to Gold within three months.

Teaching Features

Extensive engineering experience from leading technology companies:
Combines ACM/USACO past problems with industry-level coding standards.

Dynamic difficulty adaptation system:
Identifies students' weak areas in real time and adjusts the training plan accordingly.

Competition mindset training:
Provides pressure-training modules designed around the IOI and USACO competition formats.

Instructor W

Academic Background

Instructor W holds a master's degree in Mathematics Education and a bachelor's degree in Mathematics from East China Normal University.

She previously taught at a leading high school in China, where she was responsible for mathematics teaching and mathematical modeling training.

The mathematical modeling course she developed won the title of Municipal Excellent Course. She was also selected as one of the Top 20 Seed Teachers in Mathematical Modeling at the China Future Education Conference.

She served as an instructor for the Jingwan Mathematical Modeling Summer Camp in 2021.

Coaching Achievements

2023

3 teams received Finalist Awards in the IMMC International Round
1 team received a Meritorious Award
1 team received an Honorable Mention
3 teams received Finalist Awards in the IMMC China Winter Round
1 team received a Meritorious Award in MCM
1 team received a Finalist Award in SJMMA

2022

1 team received the HiMCM Outstanding Award
3 teams received Finalist Awards
1 team received a Meritorious Award
1 team received an Honorable Mention
2 teams received Finalist Awards in the IMMC China Autumn Round
1 team received an Honorable Mention

2021

1 team received the HiMCM Outstanding Award
2 teams received Finalist Awards
2 teams received Meritorious Awards
1 team received an Honorable Mention

2020

1 team received a HiMCM Finalist Award
2 teams received Meritorious Awards

Student Results

2025

1 team received the Outstanding Award
3 teams received Finalist Awards
3 teams received Meritorious Awards
6 teams received Honorable Mentions

2024

3 teams received Finalist Awards
6 teams received Meritorious Awards
10 teams received Honorable Mentions

2023

5 teams received Finalist Awards
6 teams received Meritorious Awards
12 teams received Honorable Mentions

2022

1 team received the Outstanding Award and was also recognized as an NCTM Winner, one of the strongest Outstanding teams, with only two selected each year
6 teams received Finalist Awards
5 teams received Meritorious Awards
11 teams received Honorable Mentions

2021

1 team received the Outstanding Award
3 teams received Finalist Awards
5 teams received Meritorious Awards
11 teams received Honorable Mentions

2020

1 team received the Outstanding Award
3 teams received Finalist Awards
6 teams received Meritorious Awards
13 teams received Honorable Mentions

2019

1 team received the Outstanding Award
4 teams received Finalist Awards
Several teams received Meritorious Awards and Honorable Mentions

2018

2 teams received Finalist Awards for Problem B, the highest recognition among Chinese-region teams
More than 10 teams received Finalist Awards for Problem A
Several teams received Meritorious Awards and Honorable Mentions

HiMCM Full-Course Coaching Program

Course Description

This is a full-course coaching program designed for students preparing for HiMCM.

The course covers mathematics, computer science, teamwork, modeling methods, data analysis, programming, and academic writing.

Through this program, students will learn how to use mathematical methods effectively to solve real-world problems. They will also gain a clear understanding of the requirements, evaluation criteria, and writing standards of mathematical modeling competitions.

The teaching team consists of experienced mathematical modeling coaches. Students will learn how to select, integrate, and process data; how to build suitable mathematical models based on real-world problems; and how to clearly communicate their ideas and solutions.

The program also includes team-based projects to help students develop collaboration skills and leadership ability.

Enrollment Requirements

Full-time high school students in Grade 9 or above are eligible to apply. Students should have a foundation in mathematics competitions.
Students should have strong teamwork skills and the motivation to pursue excellence.
In addition to solid middle school and high school mathematics knowledge, students are encouraged to have some foundation in higher mathematics, including calculus, linear algebra, and probability theory.
Students should have basic computer programming skills.
Students should have a solid English foundation, with strong English reading and academic paper-writing ability.

Course Outline

1. Introduction to Mathematical Modeling

Introductory cases, the general process of mathematical modeling, and competition rules.

2. Programming Fundamentals: Python or MATLAB

Basic syntax of modeling programming languages, data structures, operations, logical judgment, and functions.

3. Advanced Programming: Python or MATLAB

Data analysis, visualization, and the use of important solving functions.

4. Evaluation Models I

Evaluation factors, indicator selection, data preprocessing, weight setting, Analytic Hierarchy Process, entropy weight method, and other methods.

5. Evaluation Models II

Comprehensive evaluation methods, TOPSIS method, fuzzy comprehensive evaluation, grey evaluation, and other methods.

6. Optimization Models I

Linear programming, concepts and definitions, solution methods, case studies, and programming implementation.

7. Optimization Models II

Nonlinear programming, concepts and definitions, case studies, and programming implementation.

8. Optimization Models III

Graph theory models, concepts of graph theory, graph representation, common graph algorithms, minimum spanning tree, shortest path, other algorithms, and case analysis.

9. Optimization Models IV

Approximation algorithms, simulated annealing, genetic algorithms, other algorithms, and case analysis.

10. Prediction Models I

Data fitting, data interpolation, outlier handling, and case analysis.

11. Prediction Models II

Machine learning models, supervised learning, unsupervised learning, and case analysis.

12. Prediction Models III

Time series models, moving average, ARMA, grey prediction models, and case analysis.

13. Dynamic Models I

Concepts of differential equation models, concepts of difference equation models, common single-variable differential equations, and case analysis.

14. Dynamic Models II

Systems of differential equations, common differential equation modeling methods, epidemic models, population competition and predator-prey models, and other models.

15. Probability Models I

Monte Carlo simulation, Markov processes, and case analysis.

16. Probability Models II

Queuing theory models and case analysis.

17. Pre-Contest Simulation

Mock contest problem discussion and analysis.

18. Simulation Feedback and Writing Guidance

Feedback on mock contest performance and guidance on academic paper writing.

Learn More About the Course

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