Executive Committe Member

• Feb 2023 - Present

Summer Research Intern

• May 2023 - Aug 2023

I collaborated closely with esteemed faculty during an enriching research internship, gaining invaluable hands-on experience in developing cutting-edge solutions utilizing Graph Neural Networks (GNNs). A significant aspect of my work involved contributing to the DiSCoMaT project—an innovative Graph Neural Network model designed to extract composition information from materials science articles. This model enhances the interpretation of material compositions within articles. Furthermore, I skillfully performed post-processing tasks on the data matrix, leveraging the power of the confusion matrix to calculate essential metrics such as recall, precision, and F1 score. Show less

Project Intern

• May 2023 - Jun 2023

I implemented coreset algorithms to enhance Deep Learning model efficiency through sample reduction. Employing coreset-based K-Means, I achieved a 10x efficiency boost in image segmentation. Furthermore, I extended this approach to neural networks, adapting the sequential coreset algorithm initially designed for logistic regression.

Teaching Assistant

• Apr 2023 - Jun 2023

I provided valuable support as a Teaching Assistant for the Probability & Stats course, benefiting over 300 students. I offered personalized assistance through office hours, addressing their queries and offering guidance. Additionally, I meticulously graded assignments and exams for 50+ students, delivering constructive feedback to enhance their performance.