Open to Opportunities

Software
Developer

Computer Science Specialist @ UofT|

Building scalable web applications with modern technologies. Specializing in full-stack development, database optimization, and cybersecurity solutions.

TypeScriptPythonReactNode.jsPostgreSQLDocker
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About Me

Get to know more about my background and expertise

University of Toronto

Honours Bachelor of Science (HBSc)

Computer Science Specialist, Minor in Mathematics

September 2023 – May 2027

Mississauga, ON

I'm a third-year Computer Science Specialist at the University of Toronto. I have experience working in cybersecurity and full stack development. I have experience working with TypeScript, Python, Java, C, React, Express, Flask, PostgreSQL and MongoDB.

Recently, I interned in cybersecurity at Polytechnique Montréal, where I'm helping translate industry needs into an insider-threat detection prototype and a real-time policy distribution system. Previously, I worked in full-stack development at JamSocial, where I engineered a scalable email communication system, developing modular React/Tailwind components and a TypeScript/Node.js backend API to support upcoming event notifications, reminders, and user engagement campaigns.

Problem Solver

Optimizing systems for performance and scalability

Security Focused

Building secure, reliable systems

Technical Skills

Languages

TypeScriptPythonJavaCSQL (PostgreSQL)NoSQL (MongoDB)HTML/CSS

Frameworks

ReactNode.jsExpressFlaskJavaFXTailwindCSS

Developer Tools

GitDockerBashUnix/LinuxPostmanSupabaseVercelRender

Libraries

Mongoosepandasscikit-learn

Work Experience

My professional journey and achievements

Database Developer Intern

Global Cleantech Directory

RemoteSeptember 2025 – Present
  • Designed and implemented a scalable SQL database for public cleantech company data, improving query efficiency by 25% through optimized indexing and schema design
  • Developed and optimized queries for high-frequency data retrieval, reducing average query runtime from 2.5s to 0.4s under sample load testing
  • Validated database performance under simulated workloads, demonstrating scalability to handle projected data growth

Cybersecurity Intern

Polytechnique Montréal

Montreal, QCMay 2025 – August 2025
  • Collaborated with industry partners Desjardins, BNC, Qohash, and Mondata to develop an insider-threat detection model, achieving 85% detection accuracy by translating operational requirements into process-mining pipelines and OPA policy bundles
  • Designed and implemented a workflow-based access control (WBAC) system on top of OPA and OPAL, extending ABAC with process-aware constraints that reduced unauthorized task execution by 40% during testing
  • Engineered real-time dynamic authorization pipeline using OPAL and OPA, by containerizing in Docker and automating Git-backed Rego policy distribution, resulting in consistent authorization across all services

Full Stack Developer Intern

JamSocial

RemoteMarch 2025 – May 2025
  • Developed and refactored 15+ modular, responsive email templates and components with React and Tailwind CSS, reducing styling redundancy and ensuring 100% rendering consistency across desktop and mobile clients
  • Built Node.js/Express/TypeScript backend API for subscription/opt-out workflows and click/open tracking, improving subscription processing efficiency by 35% and enabling real-time engagement analytics for 1k+ active users
  • Developed and maintained Postman and MongoDB Compass integration tests to validate API endpoints and database operations, reducing production defects by 35% and increasing system reliability ahead of deployments

Featured Projects

Showcasing my technical skills through real-world applications

Flight Booking Web Application

A full-stack flight booking system with dynamic search, real-time availability, and seamless booking experience.

  • Developed frontend interactivity with Node.js, creating a dynamic, user-friendly interface for easy flight searching and booking
  • Implemented search functionality using Python and Flask, connecting frontend and backend seamlessly with RESTful APIs
  • Improved search queries by 40% through efficient data retrieval strategies in PostgreSQL
JavaScriptPythonFlaskNode.jsPostgreSQL

MYSH Linux Shell

A custom Unix/Linux shell implementation with advanced features including pipelines, networking, and process management.

  • Delivered a Linux/Unix shell with built-in commands, pipelines, background jobs, and process management validated by automated tests
  • Created networking commands enabling multi-client chat over TCP sockets with message routing and user IDs
  • Engineered heap-based variable storage with safe allocation, validated with valgrind and gdb to eliminate memory leaks
CBashUnix/LinuxSocketsNetworking

Weather Notification Script

Python-based application providing tailored weather notifications using OpenWeatherMap API with personalized messaging sent via Pushover.

  • Integrated multiple APIs (OpenWeatherMap, Pushover, OpenAI) to fetch weather data, generate personalized messages, and deliver notifications
  • Implemented geocoding to retrieve latitude/longitude data and fetch accurate weather conditions including temperature, wind speed, and sky conditions
  • Designed modular architecture supporting both AI-generated and preset messages for flexible notification customization
PythonOpenWeatherMap APIPushover APIOpenAI APIREST APIs

Paint Application

Java-based painting application built with JavaFX following the Model-View-Controller architecture for improved modularity.

  • Developed using MVC architecture, improving modularity and maintainability of the codebase
  • Implemented collaborative version control workflows with Git using feature branches and pull requests, reducing merge conflicts by 30%
  • Enhanced team productivity by introducing code reviews and Git branching strategies, cutting integration time by 25%
JavaJavaFXMVCGit

Student Success Predictor

Machine learning model that predicts student course performance based on study habits, attendance, and extracurricular participation.

  • Developed machine learning model using Random Forest Classifier to predict student pass/fail outcomes
  • Improved baseline accuracy by 27% through hyperparameter fine-tuning and optimized feature selection
  • Enabled real-time predictions to help users make informed decisions based on student performance metrics
PythonPandasScikit-learnJupyterRandom Forest

Want to see more of my work?

View My GitHub

Get In Touch

I'm always open to discussing new opportunities, collaborations, or just having a chat about tech!

Email

nafay.tashfeen@mail.utoronto.ca

Phone

437-599-4041

Location

Mississauga, ON

Send Me a Message

Currently looking for internship and full-time opportunities in full-stack development, database engineering, and cybersecurity. Let's build something amazing together!