- Bio
-
Iām a Computer Science student at York University with a passion for AI, software development, and problem-solving. Currently, Iām gaining hands-on experience in machine learning, chatbot development, and API integration through Riipen Level UP projects.
My skills include Python, Java, Flask, NLP, databases, and cloud deployment (Azure, Docker). I enjoy working on real-world applications that bridge technology and industry needs. Excited to learn, collaborate, and build innovative solutions!
- Resume
- Riipen_Resume.pdf
- Portals
-
-
Vancouver, British Columbia, Canada
-
- Categories
- Artificial intelligence Databases Machine learning Software development Website development
Skills
Latest feedback
Achievements
Recent projects
Work experience
Busser
Moxies
October 2023 - Current
Maintained a fast-paced environment, ensuring smooth restaurant operations.
Collaborated with kitchen and service staff, improving teamwork and time management.
Education
Bachelor of Science (B.S.), Computer Science
York University
September 2023 - April 2027
Personal projects
Happy Nutrition ā Health Academy Enhancement
February 2025 - March 2025
https://healthacademy.caFocus: Web Development, AI Integration, UX Design
Collaborated with a student team to enhance an online learning platform for personalized nutrition guidance.
Designed an interactive, user-friendly interface and explored integration of a Large Language Model (LLM) to deliver tailored health advice.
Website Projects
January 2025 - February 2025
Portfolio Website for an Artist
Designed and developed a professional portfolio website tailored for an artist. The website features an intuitive navigation system that includes sections such as Home, About, Portfolio, and Contact. The gallery showcases the artist's work in a visually appealing layout, offering users an immersive experience. The site is optimized for performance and ease of use, ensuring a seamless browsing experience across all devices.
Machine Learning MVP Predictor
June 2021 - July 2021
Developed a machine learning algorithm to predict the NBA MVP based on player statistics and historical data. Utilized Python libraries for data analysis, model training, and feature extraction. Applied Pandas for data splicing, formatting, and preprocessing to optimize model learning.