CS undergrad. End-to-end ML pipelines, async backends, generative models. Python · PyTorch · XGBoost · FastAPI · Redis · Docker. Open to 2026 AI/ML internships
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
F1-Predict
June 13, 2026 – Present
Can machine learning predict Formula 1? This project uses historical race data, driver and team performance metrics, circuit trends, weather conditions, and race simulations to forecast Grand Prix outcomes.
View ProjectClearAudit
February 26, 2026 – Present
End-to-end fraud detection for Vietnam's financial ecosystem. Dual-branch ML (XGBoost + Autoencoder) with 99.7% recall across 4 fraud typologies. CTGAN/WGAN-GP data synthesis, 32 DuckDB-engineered features, real-time FastAPI + Redis inference, SHAP explainability & interactive dual-audience frontend.
View ProjectCode_executor_Engine
January 20, 2026 – Present
SWE Intern - Backend | Take-home Assignment (Live Code Execution)
View ProjectGradConnect
April 7, 2025 – April 7, 2025
GradConnect is a web application built with Express.js that helps students connect, collaborate, and manage academic activities efficiently. It features user signup pages, a structured frontend with static assets, secure authentication routes, and a PostgreSQL database integration for data handling. Designed to be modular and easy to scale
View ProjectWeather-API
March 31, 2025 – March 31, 2025
A modern weather app with a glassmorphic UI that displays real-time weather conditions for any city using the OpenWeatherMap API. Features include current temperature, weather descriptions, and daily highs/lows in a responsive, mobile-friendly design.
View ProjectStoreScout
December 25, 2024 – April 23, 2025
An automated Python script that checks your personal Valorant store daily and captures screenshots for tracking item rotations.
View ProjectCultural Fit Analysis
The candidate's personal projects demonstrate initiative and a self-driven learning approach. The variety of projects, from web development to machine learning and scripting, suggests adaptability and a willingness to explore different technical domains. This aligns with a culture that values continuous learning and diverse problem-solving.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions indicate an ability to work independently on personal projects.