Sichen Chen

• Research on biological topics including the growth of pollen tube tips from various types of flowers with biologists. Conducted in-depth analysis on a dataset comprising over 20K data points using statistical techniques and visualization tools. • Developed a novel Convolutional Neural Network (CNN) approach using PyTorch to uncover the model of pollen tube tip growth. Validated the efficacy of the model with upcoming data sets. • Employed advanced hypothesis testing techniques, like Kolmogorov–Smirnov test, to assess the significance of our findings derived from observed data. • Effectively communicating results to biologists and offering data-driven insights and recommendations aids in their comprehension of the mechanism underlying pollen tube tip growth.

Demonstrated fundamental statistical concepts such as probability theory, hypothesis testing, and principles of linear regression. • Imparted programming skills in data analysis, visualization, inference, and prediction tasks using R. • Developed and evaluated comprehensive assignments closely aligned with real-world applications to reinforce learning.