CV

Curriculum vitae of Yuhe Qin.

Contact Information

Name Yuhe Qin
Professional Title B.Sc. Mathematics and Applied Mathematics
Email qinyuhe666@gmail.com
Location , Shenzhen, Guangdong

Professional Summary

Undergraduate in the Fields Honors Class in Mathematics at SUSTech, with research experience in topological data analysis, topology-aware point cloud learning, and computational topology for signal representation.

Education

  • 2022 - 2026

    Shenzhen, China

    B.Sc.
    Southern University of Science and Technology
    Mathematics and Applied Mathematics
    • Fields Honors Class in Mathematics.
    • Relevant coursework: Machine Learning, Optimization, Topology, Probability and Statistics, Mathematical Analysis, Linear Algebra.
  • 2025 - 2025

    Berkeley, CA, USA

    University of California, Berkeley
    Visiting Student
    • Relevant coursework: Efficient Algorithms and Intractable Problems, Data Structures, Artificial Intelligence, Structure and Interpretation of Computer Programs, Discrete Mathematics.

Teaching Experience

  • 2026 - 2026

    Shenzhen, China

    Teaching Assistant and Grader, Calculus II
    Southern University of Science and Technology
    Spring 2026
    • Prepared and delivered weekly recitation and review sessions in English for a class of 37 students.
    • Reviewed common mistakes from previous homework and guided students through in-class practice problems.
    • Held office hours and graded homework, providing individualized support and feedback on students’ performance.
    • Course materials: Calculus II TA folder

Research Experience

  • 2025 - 2026

    Shenzhen, China

    Bachelor's thesis: Topology-Aware Learning for 3D Point Clouds
    Southern University of Science and Technology
    Supervised by Prof. Yifei Zhu
    • Analyzed geometric and topological inductive biases in deep learning frameworks, evaluating persistent homology for robust feature extraction in unstructured 3D vision data.
    • Proposed a two-axis taxonomy based on representation mechanisms and integration strategies for topology-aware point cloud learning.
    • Designed and executed empirical ablation studies using PointNet backends to quantify performance gains from multi-modal feature fusion.
  • 2024 - 2024

    Shenzhen, China

    Research Intern, Computational Topology and Signal Representation
    Southern University of Science and Technology
    Supervised by Prof. Yifei Zhu
    • Studied topological data analysis with a focus on persistent homology and its computational pipeline.
    • Reviewed auditory Gestalt principles and explored mathematical formulations of perceptual grouping for signal representation.
    • Designed preliminary experiments converting speech signals into topology-based embeddings.
  • 2024 - 2024

    Oxford, England

    Poster Presenter
    SPIRES 2024
    • Presented a poster on topology-enhanced machine learning for classifying voiced and voiceless consonants in speech signals.

Academic Experience

  • 2023 - 2024

    Seminar Speaker
    Student Seminars in Mathematics
    • Delivered advanced seminar talks on set theory (the Axiom of Choice), Fourier analysis (Poisson summation, uncertainty principles), and extremal combinatorics.

Projects

  • Interactive Vietoris–Rips Filtration and Persistence Diagram Demo

    Web-based computational topology visualization tool (May 2026, ongoing).

    • Built an interactive demo for Vietoris–Rips filtrations and persistence diagrams on 2D point clouds.
    • Visualized H₀, H₁, and H₂ features with birth–death pairs and persistence filtering.
    • Added hover interactions linking simplices, birth–death pairs, and persistence features.
  • Artificial Intelligence Projects

    Fall 2025, UC Berkeley.

    • Implemented Markov Decision Processes and Q-learning architectures for multi-agent planning.
    • Built neural network classifiers from scratch in Python, focusing on optimization methods and gradient descent variants.
  • Semantic Segmentation for Autonomous Driving

    Spring 2023.

    • Developed Fully Convolutional Networks for pixel-level semantic segmentation.
    • Optimized cross-entropy loss functions and evaluated via mIoU metrics.
  • Data Structures and Algorithms Projects

    Spring 2025, UC Berkeley.

    • Built Java projects involving custom data structures, graph search, corpus analysis, and WordNet-style semantic queries.

Skills

Programming (): Python, C++, Java, C#, Scheme, SQL, HTML/CSS
Machine Learning (): PyTorch, NumPy, SciPy, Matplotlib, GUDHI
Tools (): LaTeX, Git, Blender, Figma, Unity, Unreal Engine