I am a Neural Network Engineer with a strong background in mathematics and data science. I hold a Master’s degree in Data Science from the University of Bristol and a Bachelor’s degree in Mathematics from Northwestern Polytechnical University. My experience includes building supervised and unsupervised learning models, working with BERT for sentiment analysis, CRF with BiLSTM for sequence labeling, and CNNs for image recognition. My experience also includes working with Liquid Time Constant (LTC), Quantized, and Spiking Neural Networks, along with research into the double descent phenomenon. I am passionate about solving complex problems in AI and machine learning, and I am eager to apply my skills to innovative projects.我是一名神经网络工程师，拥有深厚的数学和数据科学背景。我拥有布里斯托大学数据科学硕士学位和西北工业大学数学学士学位。我的经验包括构建监督和无监督学习模型、使用 BERT 进行情感分析、使用 CRF 与 BiLSTM 进行序列标记以及使用 CNN 进行图像识别。我的经验还包括使用液体时间常数 (LTC)、量化和尖峰神经网络，以及对双下降现象的研究。我热衷于解决人工智能和机器学习中的复杂问题，并且渴望将我的技能应用于创新项目。

I am also proficient in data visualization using Tableau and Python. I have created a wide range of visualizations to present complex data in clear, insightful ways, utilizing Python libraries such as Matplotlib and Seaborn. I excel at transforming raw data into actionable insights through visually compelling dashboards and reports. My combined skills in neural networks and data visualization make me adept at both model building and interpreting results for effective decision-making.