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.
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.