Just graduated from Toronto Metropolitan University with a Bachelor of Science Honours in Computer Science. I specialize in making Full-stack web applications. I have also had the opportunity to dive into many different fields, some of which include: Machine Learning, A.I., Cloud Engineering, DevOps, and Robotics. Looking forward to future opportunities!
About Me
Portfolio Work
Some of the cool projects I've worked on.
JWT Authentication Project (Java Spring Boot, React Typescript, PostgreSQL)
Implemented stateless JWT‑based authentication
Built a Spring Boot security filter that extracts, validates, and parses JWTs on every request—no server‑side sessions—so each API call carries its own credentials and scales horizontally.Secured tokens in HttpOnly, Secure, SameSite cookies
Packaged access tokens into cookies that JavaScript can’t read (HttpOnly), only transmit over HTTPS (Secure), and block on third‑party contexts (SameSite=strict), mitigating XSS and CSRF risks.Configured CORS and endpoint guards
Locked down /api/v1/auth/** as publicly accessible, enforced .anyRequest().authenticated() for all others, and whitelisted only the frontend origin via Spring’s WebMvcConfigurer to prevent unauthorized cross‑origin calls.
Real Estate and Facebook Automation Script (Python, Selenium)
Developed a Python script using Selenium to scrape data from a real estate website, extracting detailed property listings including address, property type, price, and amenities.
Automated the process of posting listings to Facebook Marketplace, utilizing a proxy server and reducing manual input time by 90%.
Designed and built a user-friendly web interface for streamlined data entry, enhancing user experience, and reducing workflow bottlenecks.
Engineered a machine learning pipeline using LSTM neural networks to forecast stock prices based on historical market data.
Automated data collection and preprocessing using Python and Yahoo Finance API to streamline the training process.
Improved prediction accuracy through hyperparameter tuning and model optimization, reducing error in stock price forecasts.
Deployed predictive scripts for real-time closing price forecasts, enhancing decision-making for financial analysis.
Stock Price Prediction System (Python, Tensorflow)
Developed a Spring Boot REST API that queries a PostgreSQL database for player, team, and game statistics, returning search results in under 200 ms.
Designed a clear PostgreSQL schema (players, teams, seasons, game logs) and used Spring Data JPA to keep SQL code readable and maintainable.
Built a React front-end featuring an auto-complete search bar and responsive stat cards so users can instantly view career, season, and per-game metrics..
NBA Stat Finder (Java Spring Boot, PostgreSQL, React, Bootstrap)
PrivyTune (Spring Boot, React, Typescript, PostgreSQL, AWS)
Designed & built a WebGPU-powered web app that downloads quant-sharded INT4 models (e.g., Phi-3-mini) into IndexedDB and runs real-time LoRA fine-tuning entirely in-browser—zero server-side GPU, maintaining 100 % data privacy for end users.
Made Spring Boot REST API (JWT-secured) exposing /models, manifest proxy, presigned S3 uploads, and audit endpoints; integrated with PostgreSQL & Flyway for version-tracked schema (users, models, license acceptances, adapter shares).
Automated a Python sharding pipeline that quantizes, splits, checksums, and publishes model weights; CI/CD uploads shards & manifest to S3, invalidates CloudFront, and updates versioned metadata.
Engineered a responsive React + TypeScript front-end with Bootstrap and Web Workers: drag-and-drop dataset ingestion, model-selector dropdown auto-populated from the API, and IndexedDB-backed caching that enables instant offline reloads and <2 s cold-start inference on supported GPUs.
Company: Royal Lepage Signature Realty
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(Currently still in development)
Get in touch
Phone
647-606-5810
martinivkamburov@gmail.com