STEM Student Spotlight: Stanley Wang, University of McGill
Welcome to our STEM Student Spotlight series, where we highlight the incredible initiatives and achievements led by STEM students in Canada. Our mission is to empower and inspire the next generation of engineers and innovators by celebrating the achievements of student leaders. In today’s spotlight, we’re excited to showcase Stanley Wang! Stanley is a student from McGill University (located in Quebec), and he participated in our Work Integrated Learning opportunity.
Stanley shared his insights into the projects he worked on and what he learned
“During my summer program, I explored supervised machine learning, which involves learning a mapping between input data and output labels based on labeled datasets. I experimented with different tasks like regression and classification, and I found that choosing the right approach depends on the problem and available data. In regression, I aimed to predict continuous values for a dependent variable based on one or more independent variables. I tried out various regression tasks, like linear regression, polynomial regression, and multivariate regression. I realized that data preprocessing is essential for accurate analysis, as the quality of input data can directly impact the model’s performance.”
“While working with linear and polynomial regression, I discovered that higher-degree polynomials can fit the data better, but they may lead to overfitting and reduced generalization on new data. I also explored the Reciprocal Logarithmic Model, which proved useful for handling non-linear relationships between variables and increased variability in the dependent variable.”
“Moreover, I got to learn about Principal Component Analysis (PCA), a technique used for reducing the number of features in a dataset. PCA helps identify the most important features that capture the majority of data variation, projecting it onto a lower-dimensional space.”
“In the realm of classification, I tackled binary classification, where the goal is to classify examples into one of two classes. In a Natural Language Processing Machine Learning project, I used binary classification to classify the sentiment (positive/negative) of news articles. However, I also encountered imbalanced datasets, where the number of examples in each class is unequal. Handling this issue is crucial for achieving accurate and unbiased classification results.”
When asked about applying what he learned to his field, Stanley said:
“My newfound understanding of machine learning techniques like supervised learning, regression, and classification empowers me to tackle real-world problems using data-driven approaches. Moreover, being aware of challenges like imbalanced datasets allows me to make informed decisions and develop fair and inclusive solutions.”
Stanley’s journey showcases the potential of young minds in the field of STEM. By sharing his experiences and knowledge, we hope to inspire other students like Stanley to pursue their passion for engineering and innovation, driving positive change in the world of technology and beyond. Stay tuned for more inspiring stories from our STEM Student Spotlight series!
