I am a dedicated and highly skilled software engineer with a strong academic background and hands-on experience in software development, research, and machine learning. Currently, I am pursuing a Master of Computer Science at the University of Illinois at Urbana-Champaign, maintaining a perfect GPA of 4.00. Previously, I earned my Bachelor of Science in Computer Science from Queen’s University with a high GPA of 4.05.
Throughout my internships and work experiences, I have developed expertise in building scalable software solutions, including a RAG-based Text-to-SQL system and tools for data analysis and visualization. I have contributed to projects that leverage advanced machine learning techniques such as Transformer-based GANs for synthetic data generation and CNN models for traffic sign classification.
I am proficient in multiple programming languages including Java, Python, C/C++, SQL, and JavaScript, and familiar with frameworks and tools such as Git, Docker, GCP, PyTorch, FastAPI, and React. My certifications in AWS Machine Learning and Azure AI Fundamentals further demonstrate my commitment to continuous learning and professional growth.
I have a passion for developing innovative AI-powered applications, as evidenced by my AI Tour Guide PWA project that integrates image recognition with GPT-powered dialogue for real-time user interaction. I am also experienced in deploying secure backend services using technologies like Nginx and TLS.
My work has been recognized through awards such as 3rd Place at the HERE Technologies Hackathon 2025 and consistent inclusion on the Dean's Honor List. I have also contributed to academic publications in the field of AI and cybersecurity.
I am eager to apply my skills and knowledge to challenging software engineering roles where I can contribute to impactful projects and continue advancing my expertise in AI and software development.
Led the setup of promptfoo for systematic evaluation of Text-to-SQL models, reducing manual query validation. Built a RAG-based Text-to-SQL system using PostgreSQL, Redis caching, and Kubernetes-deployed services focusing on retrieval prompts, evaluation, and API integration.
Generated realistic, privacy-preserving synthetic time series data using a customized Transformer-based GAN. Validated synthetic data fidelity via PCA and t-SNE ensuring statistical alignment with real-world distributions.
Cleaned and analyzed student admissions data using Python, performed EDA, produced visualizations and internal reports. Developed an internal tool to evaluate candidates by calculating and ranking resumes against job descriptions.
Built a Python tool with rule-based parsing to translate natural language documentation into SQL templates, reducing manual effort by 70%. Developed an interactive web dashboard to visualize financial data improving stakeholder decision-making.
Master of Computer Science
GPA: 4.00/4.00
Bachelor of Science in Computer Science
GPA: 4.05/4.30
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