Machine Learning Data Science

Kaiyue He

484-***-**** ******@***.***

PhD candidate with a strong foundation in mathematics and computational methods. Highly proficient in Python (NumPy, Pandas, PyTorch) and SQL, with extensive experience in data science, machine learning, and statistical modeling. Adept at developing algorithms, optimizing code, and delivering production-ready data solutions. Proven track record of solving complex problems independently. Excited to leverage these skills to drive data insights and innovation. 1 Skills

• Programming: Python (NumPy, Pandas, PyTorch), R, SQL

• Data Science: Statistical Modeling, Machine Learning, Data Cleaning, Feature Engineering, Data Pipelining

• Tools: MySQL, Git, Ubuntu Linux

• Mathematics: Linear Algebra, Probability & Statistics, Numerical Analysis, Optimization

• Soft Skills: Problem-Solving, Collaboration, Communication, Independent Work 2 Experience

2.1 Ph.D. Research in Mathematics

Syracuse University, Syracuse, NY 2021–Present

• Conducted research focused on algebra and algebraic geometry. Used Macaulay2, a specialized computational tool, to design and implement algorithms for solving complex mathematical problems

• Collaborated with peers and presented results at seminars, demonstrating strong communication and teamwork skills.

2.2 Internship — FEDIMOSS

shengjian.net Summer 2023

• Analyzed and optimized open-source codebases, improving software performance and scalability for production environments.

• Applied software engineering practices to ensure maintainable, efficient data pipelines, supporting backend engineering needs.

2.3 Internship — Zhengzhou JinHui Computer System Engineering Ltd. zzjinhui.com Summer 2019

• Evaluated code complexity using metrics like cyclomatic complexity, optimizing performance and reducing debugging time.

• Enhanced software maintainability, contributing to robust, production-ready systems. 2.4 Instructor of Record — Syracuse University

Syracuse, NY 2019–2024

• Taught Probability & Statistics and Calculus, integrating data-driven examples to enhance learning.

• Developed and communicated complex statistical concepts clearly, fostering analytical skills in stu- dents and demonstrating collaboration potential.

3 Selected Projects

3.1 Kaggle Competition: [Store Sales - Time Series Forecasting] 2025

• Developed a machine learning model to predict the unit sales.

• Applied advanced feature engineering and data cleaning techniques.

• Delivered a production-ready solution.

4 Education

Ph.D. in Mathematics Expected May 2025

Syracuse University, Syracuse, NY

Advisor: Graham J. Leuschke

Relevant Coursework: Advanced Statistics, Machine Learning, Data Mining M.S. in Mathematics May 2021

Syracuse University, Syracuse, NY

B.A. in Mathematics May 2018

Brandeis University, Waltham, MA

5 Selected Conferences

• Algebra Day, George Mason University, 2024

• Route 81 Conference in Commutative Algebra, Syracuse University, 2023

• Fellowship of the Ring Seminar, Virtual, 2022–2023

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