University of Ottawa – Ottawa, Ontario
Bachelor of Science (Honors), Computer Science
Graduated April 2024 | Cum Laude
During my studies, I developed a strong foundation in core computer science topics like data structures, databases, networking, and operating systems. I especially gravitated toward artificial intelligence, machine learning, and web systems — which led to multiple course-based projects and GitHub contributions.
Explore some of my noteworthy projects that showcase my skills and passion for technology.
Using Python, NLTK, and the Vector Space Model, I built an Information Retrieval system. It processed 322 documents, tokenizing them and removing stop words, creating a substantial inverted index of 450k words. Employing TF-IDF weighting and cosine similarity, the system efficiently ranked documents for 50 test queries, resulting in an effective retrieval system.
I created a Java-based Decision Tree algorithm from scratch, improving data analysis efficiency for a large dataset. The model achieved a 90% accuracy rate and reduced analysis time by 50% through the implementation of the Shannon entropy algorithm. You can find the project and documentation on my GitHub repository.
I implemented image classification using a Convolutional Neural Network (CNN) in Python with TensorFlow, focusing on the CIFAR-10 dataset. The CNN successfully classified images, demonstrating its ability to recognize objects from this diverse dataset.
I implemented the DBSCAN algorithm in Java for GPS coordinate clustering, reducing processing time by 15% and enhancing outlier detection. This experience deepened my expertise in computer vision, object-oriented programming, and algorithm optimization, contributing to my personal and professional growth.
I developed and implemented various cryptographic algorithms in Python, including Caesar Cipher, RSA, and AES encryption. This project deepened my understanding of encryption, decryption, and key management principles, while strengthening my grasp of secure communication and data protection practices.
I installed and configured Pi-hole on Debian Linux within VirtualBox, boosting network security by 20% and reducing page load times by 40%. By routing DNS queries through Pi-hole, I achieved a 30% improvement in web page loading times and effectively blocked unwanted ads and pop-ups, showcasing my ability to enhance network performance and security.
Led Social Network Analysis on GitHub user data with 37,000 nodes. Employed Graph Neural Network (GNN), Naive Bayes, and Logistic Regression for binary node classification. Attained 87.82% average accuracy with GNN and a solid 83.42% accuracy with Logistic Regression in binary classification.
My portfolio, crafted with HTML, CSS, and JavaScript and hosted on GitHub, serves as a dynamic showcase of my skills, projects, and achievements, enabling visitors to explore my expertise and accomplishments easily
Explore the certifications I’ve earned throughout my journey.
Each one represents hands-on learning and growth in areas such as cloud computing, programming, data science, and AI.
Gained hands-on expertise in designing scalable, fault-tolerant, and cost-effective architectures on AWS. Strengthened understanding of core AWS services, security, and deployment best practices.
Mastered Python fundamentals including data structures, APIs, and databases, and created practical data-driven applications as part of the Capstone Project.
Built foundational knowledge in data science and SQL, with hands-on tasks to solidify understanding of relational databases and basic data analysis workflows.
Completed this professional certificate covering data science basics, Python, SQL, and machine learning. Developed real-world projects including a capstone to apply skills end-to-end.
Applied Python for data analysis and visualization. Tackled real-world data science and machine learning problems using practical techniques.
Explored AI principles and their applications. Utilized IBM Watson AI services to create and deploy an AI-powered chatbot, gaining hands-on AI experience.
Used Scikit-learn, SciPy, and Spark for machine learning. Built deep learning models like CNNs, autoencoders, and RNNs using Keras, PyTorch, and TensorFlow.
Developed practical AI projects using IBM Watson and OpenCV. Built a customer service chatbot and applied AI for image recognition and classification tasks.
Whether you're seeking a tech enthusiast to enhance your project or are interested in a casual coffee conversation about technology, don't hesitate to get in touch!