Ojas {Singh}
Student at the University of Toronto with strong fundamentals in full-stack development
Hi there!
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<h3> This is your friendly neighbourhood full-stack developer. </h3>
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I am a currently a full-time student at the University of Toronto, in the Computer Science, Mathematics and Statistics program. I started building web applications about a year ago, and I enjoy building scaleable and impactful projects.
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I'm always learning something new. I'm currently experimenting with deep learning concepts. I primarily use Typescript for my full-stack projects, and Python for my machine-learning ones. Check out my résumé and GitHub to learn more about my work. Or shoot me a message, I love connecting with people!
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<img src='handsome-guy.png'>
Experiences
Full-Stack Developer @ ACE University of Toronto
June 2022 - Present • Part-time | Toronto, ON
- Developed a three-tier architecture web application using Next.js, Stripe, FireAuth and FirestoreDB
- Implemented a CI/CD pipeline via deployment on Vercel which was integrated with E2E monitoring on the deployment pipeline using Checkly and Next.js analytics, yielding a loaded web vital time of under 340ms
- Containerised the entire application using Docker, reducing setup time from 100+ minutes to under 10 minutes.
- Led meetings with the internal team and held standups to prioritise targeting optimal user/admin experience.
Machine Learning Engineer @ Omdena
Sept 2020 - Dec 2020 • Part-time | Singapore, SG
- Researched parameters of economic well-being using surveys, Sentinel-2 satellite imagery, and Open Street Map.
- Pre-processed data by refactoring survey data, and applying z-score standardisation using scikit-learn.
- Implemented Principal Component Analysis (PCA) using scikit-learn to compress a 12-dimensional parameter space to 2-dimensional parameter space, capturing 93% of variance from the PCA with minimal information loss.
- Led a sprint with 5 engineers as scrum-master using Jira, and presented findings to the senior engineers.
Data Science Intern @ MEIT
Sept 2020 - Dec 2020 • Full-time | Gurugram, IN
- Pre-processed dataset of 8000+ images of cards by cleaning, feature-extracting, and segmenting via scikit-image.
- Utilised Faster R-CNN to extract region-proposals and to classify handwriting via its own support vector machine
- Communicated closely with other interns and senior data scientists with best practices of Agile development.