Shiying (Florence) Wang
About Shiying (Florence) Wang
Shiying (Florence) Wang is a Data Scientist at ProCogia in Vancouver, Canada, with a Master's degree in Data Science from The University of British Columbia. She has expertise in various analysis techniques and software tools, and is fluent in English and Chinese Mandarin.
Work at ProCogia
Shiying (Florence) Wang has been employed at ProCogia as a Data Scientist since 2020. Based in Vancouver, British Columbia, she has accumulated four years of experience in this role. At ProCogia, she applies her expertise in data analysis and machine learning to various projects, contributing to the company's data-driven solutions.
Education and Expertise
Shiying Wang holds a Master of Data Science from The University of British Columbia, where she studied from 2019 to 2020 and achieved a high academic performance with a score of 92.4%. She also completed a Summer Exchange program at the University of Cambridge. Prior to her master's degree, she obtained a Bachelor of Science in Statistics from the University of California, Davis, studying from 2015 to 2019.
Technical Skills and Experience
Shiying Wang possesses extensive experience in various analysis techniques, including topic modeling with Latent Dirichlet Allocation (LDA) and time series analysis. She is proficient in using Python libraries such as Keras and TensorFlow for data analysis and machine learning tasks. Her advanced skills include working with neural network architectures, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), as well as applying transfer learning techniques.
Previous Experience
Before her current role at ProCogia, Shiying Wang worked as a Machine Learning Engineer Intern at realtor.com. This internship took place in 2020 and lasted for one month, during which she participated in the UBC Capstone Project. This experience provided her with practical insights into machine learning applications in a professional setting.
Open Source Contributions
Shiying Wang has contributed to open-source projects on GitHub, demonstrating her expertise in data science and machine learning. These contributions highlight her commitment to the field and her ability to collaborate on projects that benefit the wider community.