welcome to Yasamin Aali’s page

I am a graduate student studying my Master of Computer Science in Brock University under the supervison of Dr.Rahnuma Islam Nishat. I am passionate about applying AI, deep learning, and graph theory to optimize processes in 3D printing. I also work on NLP and data analysis projects that focus on practical applications of machine intelligence.

Business Data Analyst Intern

I worked as a Business Data Analyst Intern at Snapptrip, a dynamic company specializing in hotel bookings and ticket sales. In my role, I was tasked with analyzing data to gain insights into customer preferences, market trends, and operational efficiency. This involves extracting meaningful information from large datasets to help the company make informed decisions and enhance the overall user experience. It was an exciting opportunity that allowed me to apply my analytical skills in a real-world setting and contribute to the success of a rapidly growing travel and hospitality platform.

Optimizing Knowledge Graph Completion with LLM-Driven Evolutionary Augmentation

We proposed a novel evolutionary-based data augmentation framework that leverages LLMs such as LLaMA3.1 to generate high-quality synthetic triples paper page.

Enhancing Sentence Relatedness Assessment using Siamese Networks

We proposed a system which integrates a Siamese Network architecture with pre-trained language models, including BERT, RoBERTa, and the Universal Sentence Encoder (USE). Through rigorous experimentation and analysis, we evaluated the performance of these models across multiple languages. Our findings revealed that the Universal Sentence Encoder excels in capturing semantic similarities, outperforming BERT and RoBERTa in most scenarios. Particularly notable is the USE’s exceptional performance in English and Marathi. These results emphasize the importance of selecting appropriate pre-trained models based on linguistic considerations and task requirements publication page.

Explainable detection of online sexism (EDOS)

Online communication has brought about an increase in sexist comments and tweets, posing significant harm to women and their social,psychological, and economic well-being. Detecting whether a text is sexist or not remains a significant challenge. This study focuses on fine-grained classifications for sexist content from two popular social media platforms, Gab and Reddit, using machine learning and natural language processing techniques. The study compares multiple classification models, including simpler models like Logistic Regression and SVM, and more advanced models like ensemble methods and BERT, with the aim of developing more effective tools to detect and combat online sexism.

Association rules algorithm using financial dataset

My bachelor thesis aimed to analyze a financial dataset using R programming language and association rules algorithm. This project includes several steps, including data cleaning and preprocessing, exploratory data analysis, and building an association rules model. This model was used to identify patterns and relationships between data set variables and generate actionable insights for investors and financial analysts. The results of the analysis showed that the association rules algorithm is effective in identifying interesting patterns and relationships in the financial data set. The model identified several rules that can be used to guide investment decisions, such as the relationship between profitability ratios and stock prices. You can find more about this project on it’s github code.

Data mining algorithms using heart disease dataset

Data mining project with heart disease dataset from Kaggle using Python. Used supervised and unsupervised algorithms (k-nearest neighbor, naive bayes, logistic regression, decision tree, k-means, one-r). You can find more about this project on it’s github code.