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portfolio
Portfolio item number 2
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publications
otcmc
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Paper Title Number 5, with math \(E=mc^2\)
Published in GitHub Journal of Bugs, 2024
This paper is about a famous math equation, \(E=mc^2\)
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
深度学习的数学基础与工程实践
Graduate course, CNU, School of Mathematical Sciences, 2025
本课程全面系统地涵盖了深度学习的核心理论与实践应用。内容从环境搭建与编程基础起步，深入讲解全连接神经网络、卷积神经网络（CNN）、序列神经网络（RNN/Transformer）等核心架构，并涵盖生成式模型（如VAE、GAN、Diffusion）及深度强化学习（如PPO、DDPG）等前沿领域。通过分类、检测、预测及生成等丰富的应用场景，帮助学员构建完整的深度学习知识体系，掌握从基础算法到高级模型的实战能力。
Linear Algebra for New Engineering
Undergraduate course, CNU, School of Mathematical Sciences, 2025
新工科线性代数是为大学人工智能等新工科专业开设的一门重要的数学基础必修课程。课程包含四个部分：第一部分介绍向量的基本运算以及最常见的线性空间——欧氏空间，并在欧氏空间中通过几何方法引入行列式的概念；第二部分介绍矩阵理论以及线性方程组的系统解法，通过贴近生活的实例具象化地解释线性代数的核心功能；第三部分介绍如何从线性映射的角度重新理解线性系统，讲解内积、正交性、特征值、奇异值、对角化等线性代数的重要概念以及图和网络、马尔科夫过程、三维点云融合、图像数据压缩等大量应用实例；第四部分介绍一般的线性空间特别是无限维线性空间，在无限维线性空间理论的基础上解释傅里叶分析这一工程学利器的原理和应用，并通过识别与分类问题展示线性代数在人工智能里的重要作用。通过学习本课程，学生将掌握人工智能等新工科所必需的线性代数理论，培养抽象思维、逻辑推理和解决实际问题的综合能力，为今后的学习和工作打下必要的数学基础。